diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index b37128fc..171c5b37 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -34,7 +34,7 @@ jobs: steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 with: token: ${{ secrets.GITHUB_TOKEN }} @@ -48,7 +48,7 @@ jobs: - name: install dependencies uses: r-lib/actions/setup-r-dependencies@v2 with: - cache-version: 1 + cache-version: 2 extra-packages: | any::reticulate any::rcmdcheck @@ -63,6 +63,14 @@ jobs: extSoftVersion()["BLAS"] shell: Rscript {0} + - if: matrix.os == 'ubuntu-latest' + run: | + sudo apt-get install python3-venv + pip install --upgrade pip + python3 -m venv env + source env/bin/activate + echo "VIRTUAL ENV:" $VIRTUAL_ENV + - name: Install reticulate run: | library("reticulate") diff --git a/DESCRIPTION b/DESCRIPTION index aee01961..636e0c37 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: netZooR Type: Package Title: Unified methods for the inference and analysis of gene regulatory networks -Version: 1.4.0 -Date: 2023-06-03 +Version: 1.5.4 +Date: 2023-09-25 Authors@R: c(person("Marouen", "Ben Guebila", email = "benguebila@hsph.harvard.edu", role = c("aut","cre"), comment = c(ORCID = "0000-0001-5934-966X")), person("Tian", "Wang", @@ -24,11 +24,13 @@ Description: netZooR unifies the implementations of several Network Zoo methods Depends: R (>= 4.2.0), igraph, reticulate, - pandaR, yarn, + pandaR, matrixcalc Remotes: - stan-dev/cmdstanr + stan-dev/cmdstanr, + jnpaulson/pandaR, + cytoscape/RCy3 biocViews: NetworkInference, Network, @@ -37,8 +39,8 @@ biocViews: Transcription, Microarray, GraphAndNetwork -Imports: - RCy3, +Imports: + RCy3, viridisLite, STRINGdb, Biobase, @@ -69,9 +71,11 @@ Imports: methods, dplyr, graphics, - cmdstanr, + cmdstanr, GeneNet, - loo + loo, + rARPACK, + corpcor License: GPL-3 Encoding: UTF-8 LazyData: false diff --git a/NAMESPACE b/NAMESPACE index 851d8dcc..2a9bd8c0 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,11 +1,11 @@ # Generated by roxygen2: do not edit by hand -export(TIGER) export(adj2el) export(adj2regulon) export(alpaca) export(alpacaCrane) export(alpacaExtractTopGenes) +export(cobra) export(condorCluster) export(condorCoreEnrich) export(condorMatrixModularity) @@ -41,12 +41,13 @@ export(pandaDiffEdges) export(pandaPy) export(pandaToAlpaca) export(pandaToCondorObject) -export(prior.pp) +export(priorPp) export(puma) export(runEgret) export(sambar) export(sourcePPI) export(spider) +export(tiger) export(visPandaInCytoscape) import(AnnotationDbi, except= select) diff --git a/R/COBRA.R b/R/COBRA.R new file mode 100644 index 00000000..d07eec8f --- /dev/null +++ b/R/COBRA.R @@ -0,0 +1,82 @@ +#' Run COBRA in R +#' +#' Description: +#' COBRA decomposes a (partial) gene co-expression matrix as a +#' linear combination of covariate-specific components. +#' It can be applied for batch correction, differential co-expression +#' analysis controlling for variables, and to understand the impact of +#' variables of interest to the observed co-expression. +#' +#' Inputs: +#' @param X : design matrix of size (n, q), n = number of samples, q = number of covariates +#' @param expressionData : gene expression as a matrix of size (g, n), g = number of genes +#' @param method : if pearson, the decomposition of the co-expression matrix is compouted. If pcorsh, COBRA decomposes the regularized partial co-expression +#' +#' Outputs: +#' @return psi : impact of each covariate on the eigenvalues as a matrix of size (q, n) +#' @return Q : eigenvectors corresponding to non-zero eigenvalues as a matrix of size (g, n) +#' @return D : non-zero eigenvalues as a list of length n +#' @return G : (standardized) gene expression as a matrix of size (g, n) +#' +#' @examples +#' +#' g <- 100 # number of genes +#' n <- 10 # number of samples +#' q <- 2 # number of covariates +#' X <- X <- cbind(rep(1, n), rbinom(n, 1, 0.5)) +#' expressionData=matrix(rnorm(g*n, 1, 1), ncol = n, nrow = g) +#' +#' # Run COBRA algorithm +#' cobra_output <- cobra(X, expressionData) +#' +#' @export + +cobra <- function(X, expressionData, method = "pearson"){ + + if(!(method %in% c("pearson", "pcorsh", "dragon"))){ + stop("Only Pearson and pcor methods are supported. Please make sure to provide a valid method argument.") + } + + numSamples <- ncol(expressionData) + if(method != "dragon"){ + N <- min(ncol(expressionData),nrow(expressionData)) + } + C <- 0 + if(method == "pearson"){ + G_star <- expressionData-rowMeans(expressionData) + expressionData <- (G_star/sqrt(rowSums(G_star^2))) + expressionData <- as.matrix(expressionData) + C <- tcrossprod(expressionData) + } + if(method == "dragon"){ + if(length(expressionData) != 2){ + stop("Dragon needs two layers, please provide a list of two matrices in expressionData or consider using the pcorsh argument") + } + if(dim(expressionData[[1]])[2] != dim(expressionData[[2]])[2]){ + stop("Dragon layers in expressionData list must have the same number of samples") + } + C <- dragon(t(expressionData[[1]]), layer2 = t(expressionData[[2]]), pval=FALSE)$cov + N <- dim(expressionData[[1]])[2] + numSamples <- N + expressionData <- rbind(expressionData[[1]], expressionData[[2]]) + print(dim(expressionData)) + } + if(method == "pcorsh"){ + C <- matrix(as.numeric(pcor.shrink(t(expressionData))), dim(expressionData)[1], dim(expressionData)[1]) + } + + eigenG <- rARPACK::eigs_sym(C,N) + + Q <- eigenG$vectors + D <- diag(eigenG$values) + + hatmat <- ginv(crossprod(X))%*%t(X) + Qinv <- ginv(Q) + QinvG <- Qinv%*%(expressionData) + + est <- t(sapply(seq_len(nrow(hatmat)), function(hatmatRow){ + diag(QinvG%*%(numSamples*diag(hatmat[hatmatRow,]))%*%t(QinvG)) + })) + + list(psi=est, Q=Q, D=eigenG$values, G=expressionData) +} diff --git a/R/PANDA.R b/R/PANDA.R index 96ec9472..1acc2b58 100644 --- a/R/PANDA.R +++ b/R/PANDA.R @@ -201,4 +201,3 @@ pandaPy <- function(expr_file, motif_file=NULL, ppi_file=NULL, computing="cpu", return(output) } - diff --git a/R/PUMA.R b/R/PUMA.R index 7dcc2a48..a30d4b99 100644 --- a/R/PUMA.R +++ b/R/PUMA.R @@ -422,5 +422,3 @@ prepResult <- function(zScale, output, regulatoryNetwork, geneCoreg, tfCoopNetwo pandaObj(regNet=regulatoryNetwork, coregNet=geneCoreg, coopNet=tfCoopNetwork, numGenes=numGenes, numTFs=numTFs, numEdges=numEdges) } -pandaObj <- setClass("panda", slots=c("regNet","coregNet","coopNet","numGenes","numTFs","numEdges")) -setMethod("show","panda",function(object){print.panda(object)}) diff --git a/R/SPIDER.R b/R/SPIDER.R index e593819e..5f0dad3d 100644 --- a/R/SPIDER.R +++ b/R/SPIDER.R @@ -423,7 +423,4 @@ prepResult <- function(zScale, output, regulatoryNetwork, geneCoreg, tfCoopNetwo resList$coopNet <- tfCoopNetwork } pandaObj(regNet=regulatoryNetwork, coregNet=geneCoreg, coopNet=tfCoopNetwork, numGenes=numGenes, numTFs=numTFs, numEdges=numEdges) -} - -pandaObj <- setClass("panda", slots=c("regNet","coregNet","coopNet","numGenes","numTFs","numEdges")) -setMethod("show","panda",function(object){print.panda(object)}) \ No newline at end of file +} \ No newline at end of file diff --git a/R/TIGER.R b/R/TIGER.R index db4398e5..bf9fef83 100644 --- a/R/TIGER.R +++ b/R/TIGER.R @@ -26,6 +26,7 @@ #' @param prior A prior regulatory network in adjacency matrix format. Rows are TFs #' and columns target genes. #' @param method Method used for Bayesian inference. "VB" or "MCMC". Defaults to "VB". +#' @param TFexpressed TF mRNA needs to be expressed or not. Defaults to TRUE. #' @param signed Prior network is signed or not. Defaults to TRUE. #' @param baseline Include baseline or not. Defaults to TRUE. #' @param psis_loo Use pareto smoothed importance sampling leave-one-out cross @@ -39,6 +40,7 @@ #' @param b_alpha Hyperparameter of edge weight W. Default = 1. #' @param sigmaZ Standard deviation of TF activity Z. Default = 10. #' @param sigmaB Standard deviation of baseline term. Default = 1. +#' @param tol Convergence tolerance on ELBO.. Default = 0.005. #' #' @return A TIGER list object. #' * W is the estimated regulatory network, but different from prior network, @@ -53,23 +55,36 @@ #' @examples #' data(TIGER_expr) #' data(TIGER_prior) -#' TIGER(TIGER_expr,TIGER_prior) -TIGER = function(expr,prior,method="VB", - signed=TRUE,baseline=TRUE,psis_loo = FALSE, - seed=123,out_path=NULL,out_size = 300, - a_sigma=1,b_sigma=1,a_alpha=1,b_alpha=1,sigmaZ=10,sigmaB=1){ +#' tiger(TIGER_expr,TIGER_prior) +tiger = function(expr,prior,method="VB",TFexpressed = TRUE, + signed=TRUE,baseline=TRUE,psis_loo = FALSE, + seed=123,out_path=NULL,out_size = 300, + a_sigma=1,b_sigma=1,a_alpha=1,b_alpha=1, + sigmaZ=10,sigmaB=1,tol = 0.005){ # check data sample.name = colnames(expr) - TF.name = intersect(rownames(prior),rownames(expr)) # TF needs to express - TG.name = intersect(rownames(expr),colnames(prior)) + if (TFexpressed){ + TF.name = sort(intersect(rownames(prior),rownames(expr))) # TF needs to express + }else{ + TF.name = sort(rownames(prior)) + } + TG.name = sort(intersect(rownames(expr),colnames(prior))) if (length(TG.name)==0 | length(TF.name)==0){ stop("No matched gene names in the two inputs...") } - + #0. prepare stan input if (signed){ - prior = prior.pp(prior[TF.name,TG.name],expr) - P = prior + prior2 = priorPp(prior[TF.name,TG.name],expr) + if (nrow(prior2)!=length(TF.name)){ + TFnotExp = setdiff(TF.name,rownames(prior2)) + TFnotExpEdge = prior[TFnotExp,colnames(prior2),drop=F] + TFnotExpEdge[TFnotExpEdge==1] = 1e-6 + prior2 = rbind(prior2,TFnotExpEdge) + prior2 = prior2[order(rownames(prior2)),] + prior2 = prior2[rowSums(prior2!=0)>0,] # remove all zero TFs + } + P = prior2 TF.name = rownames(P) TG.name = colnames(P) }else{ @@ -79,7 +94,6 @@ TIGER = function(expr,prior,method="VB", n_genes = dim(X)[1] n_samples = dim(X)[2] n_TFs = dim(P)[1] - P = as.vector(t(P)) ## row=TG, col=TF P_zero = as.array(which(P==0)) P_ones = as.array(which(P!=0)) @@ -95,7 +109,6 @@ TIGER = function(expr,prior,method="VB", sign = as.integer(signed) baseline = as.integer(baseline) psis_loo = as.integer(psis_loo) - data_to_model = list(n_genes = n_genes, n_samples = n_samples, n_TFs = n_TFs,X = as.matrix(X), P = P, P_zero = P_zero, P_ones = P_ones,P_negs = P_negs, P_poss = P_poss, P_blur = P_blur, n_zero = n_zero,n_ones = n_ones,n_negs = n_negs, n_poss = n_poss, n_blur = n_blur, @@ -107,46 +120,69 @@ TIGER = function(expr,prior,method="VB", f = cmdstanr::write_stan_file(TIGER_C) # save to .stan file in root folder #mod = cmdstanr::cmdstan_model(f,cpp_options = list(stan_threads = TRUE)) # compile stan program, allow within-chain parallel mod = cmdstanr::cmdstan_model(f) - + #2. run VB or MCMC if (method=="VB"){ fit <- mod$variational(data = data_to_model, algorithm = "meanfield",seed = seed, - iter = 50000, tol_rel_obj = 0.005,output_samples = out_size) + iter = 50000, tol_rel_obj = tol,output_samples = out_size) }else if (method=="MCMC"){ fit <- mod$sample(data = data_to_model,chains=1,seed = seed,max_treedepth=10, iter_warmup = 1000,iter_sampling=out_size,adapt_delta=0.99) } - + ## optional: save stan object if (!is.null(out_path)){ fit$save_object(paste0(out_path,"fit_",seed,".rds")) + fit$save_output_files(out_path) } - + #3. posterior distributions - - ## point summary of W - W_sample = fit$draws("W",format = "draws_matrix") ## matrix, each row is a sample of vectorized matrix - one_ind = which(colSums(W_sample==0)0,] A_ij = A_ij[,colSums(A_ij!=0)>0] - return(A_ij) } @@ -309,7 +344,7 @@ prior.pp = function(prior,expr){ # stan model, conditional likelihood TIGER_C <- -' + ' data { int n_genes; // Number of genes int n_samples; // Number of samples @@ -358,35 +393,42 @@ parameters { } transformed parameters { - matrix[n_genes, n_TFs] W; // Regulatory netwrok W - vector[n_all] W_vec; // Regulatory vector W_vec + vector[sign ? n_negs : 0] W_negs; + vector[sign ? n_poss : 0] W_poss; + vector[sign ? n_blur : 0] W_blur; + vector[sign ? 0 : n_ones] W_ones; - W_vec[P_zero]=rep_vector(0,n_zero); if (sign) { - vector[n_negs] W_negs = beta3.*sqrt(alpha3); // Regulatory network negative edge weight - vector[n_poss] W_poss = beta2.*sqrt(alpha2); // Regulatory network positive edge weight - vector[n_blur] W_blur = beta0.*sqrt(alpha0); // Regulatory network blurred edge weight + W_negs = beta3.*sqrt(alpha3); // Regulatory network negative edge weight + W_poss = beta2.*sqrt(alpha2); // Regulatory network positive edge weight + W_blur = beta0.*sqrt(alpha0); // Regulatory network blurred edge weight + + }else{ + W_ones = beta1.*sqrt(alpha1); // Regulatory network non-zero edge weight + + } +} + +model { + // local parameters + vector[n_all] W_vec; // Regulatory vector W_vec + W_vec[P_zero]=rep_vector(0,n_zero); + if (sign){ W_vec[P_negs]=W_negs; W_vec[P_poss]=W_poss; W_vec[P_blur]=W_blur; }else{ - vector[n_ones] W_ones = beta1.*sqrt(alpha1); // Regulatory network non-zero edge weight W_vec[P_ones]=W_ones; } - W = to_matrix(W_vec,n_genes,n_TFs); // by column - - matrix[n_genes,n_samples] mu = W*Z; // mu for gene expression X + matrix[n_genes, n_TFs] W=to_matrix(W_vec,n_genes,n_TFs); // by column + matrix[n_genes,n_samples] mu=W*Z; // mu for gene expression X if (baseline){ - matrix[n_genes,n_samples] mu0; // baseline - mu0 = rep_matrix(b0,n_samples); - mu = mu + mu0; + matrix[n_genes,n_samples] mu0=rep_matrix(b0,n_samples); + mu=mu + mu0; } - vector[n_genes*n_samples] X_mu = to_vector(mu); vector[n_genes*n_samples] X_sigma = to_vector(rep_matrix(sqrt(sigma2),n_samples)); -} -model { // priors sigma2 ~ inv_gamma(a_sigma,b_sigma); @@ -417,10 +459,28 @@ model { } - generated quantities { - vector[n_genes*n_samples] log_lik; + vector[psis_loo ? n_genes*n_samples : 0] log_lik; if (psis_loo){ + // redefine X_mu, X_sigma; this is ugly because X_mu, X_sigma are temp variables + vector[n_all] W_vec; // Regulatory vector W_vec + W_vec[P_zero]=rep_vector(0,n_zero); + if (sign){ + W_vec[P_negs]=W_negs; + W_vec[P_poss]=W_poss; + W_vec[P_blur]=W_blur; + }else{ + W_vec[P_ones]=W_ones; + } + matrix[n_genes, n_TFs] W=to_matrix(W_vec,n_genes,n_TFs); // by column + matrix[n_genes,n_samples] mu=W*Z; // mu for gene expression X + if (baseline){ + matrix[n_genes,n_samples] mu0=rep_matrix(b0,n_samples); + mu=mu + mu0; + } + vector[n_genes*n_samples] X_mu = to_vector(mu); + vector[n_genes*n_samples] X_sigma = to_vector(rep_matrix(sqrt(sigma2),n_samples)); + // leave one element out for (i in 1:n_genes*n_samples){ log_lik[i] = normal_lpdf(X_vec[i]|X_mu[i],X_sigma[i]); diff --git a/README.md b/README.md index 1256d1d8..2b7dec09 100644 --- a/README.md +++ b/README.md @@ -81,7 +81,7 @@ netZooR currently integrates:
DRAGON -DRAGON (Determining Regulatory Associations using Graphical models on Omics Networks) Shutta et al. is a method for estimating multiomic Gaussian graphical models (GGMs, also known as partial correlation networks) that incorporate two different omics data types. DRAGON builds off of the popular covariance shrinkage method of Ledoit and Wolf with an optimization approach that explicitly accounts for the differences in two separate omics "layers" in the shrinkage estimator. The resulting sparse covariance matrix is then inverted to obtain a precision matrix estimate and a corresponding GGM. Although GGMs assume normally distributed data, DRAGON can be used on any type of continuous data by transforming data to approximate normality prior to network estimation. Currently, DRAGON can be applied to estimate networks with two different types of omics data. Investigators interested in applying DRAGON to more than two types of omics data can consider estimating pairwise networks and "chaining" them together. +DRAGON (Determining Regulatory Associations using Graphical models on Omics Networks) Shutta, Weighill et al. is a method for estimating multiomic Gaussian graphical models (GGMs, also known as partial correlation networks) that incorporate two different omics data types. DRAGON builds off of the popular covariance shrinkage method of Ledoit and Wolf with an optimization approach that explicitly accounts for the differences in two separate omics "layers" in the shrinkage estimator. The resulting sparse covariance matrix is then inverted to obtain a precision matrix estimate and a corresponding GGM. Although GGMs assume normally distributed data, DRAGON can be used on any type of continuous data by transforming data to approximate normality prior to network estimation. Currently, DRAGON can be applied to estimate networks with two different types of omics data. Investigators interested in applying DRAGON to more than two types of omics data can consider estimating pairwise networks and "chaining" them together.
@@ -89,6 +89,11 @@ netZooR currently integrates: TIGER (Transcription Inference using Gene Expression and Regulatory Data) Chen et al. is a Bayesian matrix factorization framework that combines prior TF binding knowledge, such as from the DoRothEA database, with gene expression data from experiments. It estimates individual-level TF activities (TFA) and context-specific gene regulatory networks (GRN). Unlike other methods, TIGER can flexibly model activation and inhibition events, prioritize essential edges, shrink irrelevant edges towards zero using a sparse Bayesian prior, and simultaneously estimate TF activity levels and the underlying regulatory network. It is important to note that TIGER works most appropriately with large sample size datasets like TCGA to include a wide range of TFs due to its lower rank constraint.
+
+COBRA +COBRA (Co-expression Batch Reduction Adjustment) Micheletti, Schlauch et al. is method for correction of high-order batch effects such as those that persist in co-expression networks. Batch effects and other covariates are known to induce spurious associations in co-expression networks and confound differential gene expression analyses. These effects are corrected for using various methods prior to downstream analyses such as the inference of co-expression networks and computing differences between them. In differential co-expression analysis, the pairwise joint distribution of genes is considered rather than independently analyzing the distribution of expression levels for each individual gene. Computing co-expression matrices after standard batch correction on gene expression data is not sufficient to account for the possibility of batch-induced changes in the correlation between genes as existing batch correction methods act solely on the marginal distribution of each gene. Consequently, uncorrected, artifactual differential co-expression can skew the correlation structure such that network-based methods that use gene co-expression can produce false, nonbiological associations even using data corrected using standard batch correction. Co-expression Batch Reduction Adjustment (COBRA) addresses this question by computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters that express the co-expression matrix as a function of the sample covariates and can be used to control for continuous and categorical covariates. The method is computationally fast and makes use of the inherently modular structure of genomic data to estimate accurate gene regulatory associations and enable functional analysis for high-dimensional genomic data. +
+ * Source protein-protein interaction network from [STRINGdb](https://string-db.org/) based on a list of protein of interest. * Plot one PANDA network in [Cytoscape](https://cytoscape.org/). @@ -156,7 +161,7 @@ For more details please refer to the [documentation website](https://netzoo.gith netZooR is also available through [Bioconda](https://bioconda.github.io/recipes/bioconductor-netzoor/README.html#package-bioconductor-netzoor) ```bash -conda install bioconductor-netzoor +conda install -c bioconda bioconductor-netzoor ``` ### Python binding diff --git a/_pkgdown.yml b/_pkgdown.yml index b4935e93..efc4ce33 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,61 +1,140 @@ +template: + bootstrap: 5 destination: docs reference: - title: Main functions desc: main functions to run each algorithm contents: - - panda.py - - create.condor.object - - panda.to.condor.object - - lioness.py + - pandaPy + - createCondorObject - lioness - alpaca - - panda.to.alpaca - sambar - monster - otter -- title: Plot functions - desc: function to plot the networks and community structures. + - cobra + - puma + - spider + - tiger + - runEgret + - dragon + - craneBipartite + +- title: PANDA + desc: All functions of PANDA contents: - - create.panda.style - - create.diff.panda.style - - condor.plot.communities - - condor.plot.heatmap - - starts_with("vis.") -- title: PANDA - desc: All function of PANDA - contents: - - panda.py - - panda.to.condor.object - - panda.to.alpaca - - panda.diff.edges - - create.panda.style - - vis.panda.in.cytoscape - - create.panda.style - - vis.diff.panda.in.cytoscape - - create.diff.panda.style + - pandaPy + - pandaToCondorObject + - pandaToAlpaca + - pandaDiffEdges + - visPandaInCytoscape + - createPandaStyle + - title: CONDOR - desc: All fucntions of CONDOR - contents: + desc: All functions of CONDOR + contents: + - createCondorObject - starts_with("condor") - - create.condor.object -- title: LIONESS + + +- title: LIONESS desc: All functions of LIONESS - contents: - - lioness.py + contents: + - lionessPy - lioness + - title: ALPACA desc: All functions of ALPACA contents: - starts_with("alpaca") -- title: SAMBAR + +- title: SAMBAR desc: All functions of SAMBAR contents: - starts_with("sambar") + - title: MONSTER desc: All functions of MONSTER contents: - starts_with("monster") + - title: OTTER - desc: All functions of OTTER + desc: OTTER functions + contents: + - otter + +- title : TIGER + desc: TIGER functions + contents: + - tiger + - TIGER_expr + - TIGER_prior + - adj2regulon + - priorPp + +- title : COBRA + desc: COBRA functions + contents: + - cobra + +- title: CRANE + desc: CRANE functions + contents: + - craneBipartite + - craneUnipartite + - elistToAdjMat + - elistSort + - elistRemoveTags + - elistSort + - isElist + - adjMatToElist + - el2adj + - el2regulon + - elistIsEdgeOrderEqual + - elistAddTags + - adj2el + - jutterDegree + +- title: DRAGON + desc: DRAGON functions + contents: + - dragon + +- title: SPIDER + desc: SPIDER functions + contents: + - spider + - degreeAdjust + +- title: PUMA + desc: PUMA functions + contents: + - puma + +- title: EGRET + desc: EGRET functions + contents: + - runEgret + +- title: Plotting functions + desc: function to plot the networks and community structures. + contents: + - createPandaStyle + - condorPlotCommunities + - condorPlotHeatmap + +- title: Helper functions + desc: miscellaneous function + contents: + - sourcePPI + - pandaToCondorObject + - pandaToAlpaca + +- title: Data + desc: Input example data files contents: - - starts_with("otter") + - yeast + - small1976 + - mut.ucec + - exon.size + - genes diff --git a/docs/404.html b/docs/404.html index 8ddfd96c..2357cfd3 100644 --- a/docs/404.html +++ b/docs/404.html @@ -1,193 +1,107 @@ - - - - + + + + - - + Page not found (404) • netZooR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + - - - - - - -
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Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

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Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

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Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

- - -
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

Site built with pkgdown 2.0.7.

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-

Site built with pkgdown 1.6.1.

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- -
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+ - - - + diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 8fb6c2ed..8a34bb01 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -1,161 +1,64 @@ - - - - - - - -License • netZooR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +License • netZooR + Skip to contents + +
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License

+
+

License

+ 
                    GNU GENERAL PUBLIC LICENSE
@@ -834,34 +737,22 @@ 
License

 <https://www.gnu.org/licenses/why-not-lgpl.html>.
-
- - - -
- +
-
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

Site built with pkgdown 2.0.7.

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+ - - - + diff --git a/docs/articles/ALPACA.html b/docs/articles/ALPACA.html index 684cdfad..9a0a9143 100644 --- a/docs/articles/ALPACA.html +++ b/docs/articles/ALPACA.html @@ -4,208 +4,183 @@ - + + Detecting differential modules using ALPACA • netZooR - - - + + + - - + - -
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-

Detecting differential modules using ALPACA

-

Megha Padi

+
+
+

Detecting differential modules using ALPACA

+

Megha Padi

-

2017-10-19

+

2017-10-19

- Source: vignettes/ALPACA.Rmd - - + Source: vignettes/ALPACA.Rmd +
ALPACA.Rmd

Install and load netZooR package

-# install.packages("devtools") 
-library(devtools)
-# install netZooR pkg with vignettes, otherwise remove the "build_vignettes = TRUE" argument.
-devtools::install_github("netZoo/netZooR", build_vignettes = TRUE)
+# install.packages("devtools") +library(devtools) +# install netZooR pkg with vignettes, otherwise remove the "build_vignettes = TRUE" argument. +devtools::install_github("netZoo/netZooR", build_vignettes = TRUE)
-

This vignettes can be accessed in R by using below line. when netZoooR was installed with arguments “build_vignettes = TRUE”.

+library(netZooR)
+

This vignettes can be accessed in R by using below line. when +netZoooR was installed with arguments “build_vignettes = +TRUE”.

-vignette("ALPACA",package="netZooR")
-

We will show how ALPACA can find changes in modular structure between two simulated networks. The networks both have 20 regulator nodes and 80 target nodes. The baseline network consists of two groups that are strongly connected to each other, whereas the perturbed network has weaker connections between the two groups. The two groups consist of nodes {A1-A10,B1-B40} and {A11-A20,B41-B80}. Contrasting the two networks using ALPACA identifies these two groups as being the modules that best characterize the perturbation.

-

These simulated networks is available in our public AWS S3 bucket. Change the preferred working directory to store the Example_2comm.txt file, otherwise the store directory is current working directory.

+vignette("ALPACA",package="netZooR")
+

We will show how ALPACA can find changes in modular structure between +two simulated networks. The networks both have 20 regulator nodes and 80 +target nodes. The baseline network consists of two groups that are +strongly connected to each other, whereas the perturbed network has +weaker connections between the two groups. The two groups consist of +nodes {A1-A10,B1-B40} and {A11-A20,B41-B80}. Contrasting the two +networks using ALPACA identifies these two groups as being the modules +that best characterize the perturbation.

+

These simulated networks is available in our public AWS S3 bucket. +Change the preferred working directory to store the Example_2comm.txt +file, otherwise the store directory is current working directory.

-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/Example_2comm.txt")
-simp.mat <- read.table("./Example_2comm.txt",header=TRUE)
+system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/Example_2comm.txt") +simp.mat <- read.table("./Example_2comm.txt",header=TRUE) 
-simp.alp <- alpaca(simp.mat,NULL,verbose=FALSE)
-
## [1] "Detecting communities in control network..."
-## [1] "modularity of projected graph 3.88578058618802e-16"
-## [1] "Q = 0.0263157894736842"
-## [1] "Q = 0.0263157894736842"
-## [1] "Computing differential modularity matrix..."
-## [1] "Computing differential modules..."
-## [1] "Merging 100 communities"
-## [1] 1
-## [1] 2
-## [1] 3
-## [1] 4
-## [1] "Merging 2 communities"
-## [1] 1
-## [1] "Computing node scores..."
-## [1] 1
-## [1] 2
+simp.alp <- alpaca(simp.mat,NULL,verbose=FALSE)
+
## [1] "Detecting communities in control network..."
+## [1] "modularity of projected graph 0.0210526315789478"
+## [1] "Q = 0.0263157894736842"
+## [1] "Q = 0.0263157894736842"
+## [1] "Computing differential modularity matrix..."
+## [1] "Computing differential modules..."
+## [1] "Merging 100 communities"
+## [1] 1
+## [1] 2
+## [1] 3
+## [1] "Merging 2 communities"
+## [1] 1
+## [1] "Computing node scores..."
+## [1] 1
+## [1] 2
-simp.alp2 <- simp.alp[[1]]
-simp.memb <- as.vector(simp.alp2)
-names(simp.memb) <- names(simp.alp2)
-
-simp.memb
-
##  A1_A  A2_A  A3_A  A4_A  A5_A  A6_A  A7_A  A8_A  A9_A A10_A A11_A A12_A A13_A 
-##     1     1     1     1     1     1     1     1     1     1     2     2     2 
-## A14_A A15_A A16_A A17_A A18_A A19_A A20_A  B1_B  B2_B  B3_B  B4_B  B5_B  B6_B 
-##     2     2     2     2     2     2     2     1     1     1     1     1     1 
-##  B7_B  B8_B  B9_B B10_B B11_B B12_B B13_B B14_B B15_B B16_B B17_B B18_B B19_B 
-##     1     1     1     1     1     1     1     1     1     1     1     1     1 
-## B20_B B21_B B22_B B23_B B24_B B25_B B26_B B27_B B28_B B29_B B30_B B31_B B32_B 
-##     1     1     1     1     1     1     1     1     1     1     1     1     1 
-## B33_B B34_B B35_B B36_B B37_B B38_B B39_B B40_B B41_B B42_B B43_B B44_B B45_B 
-##     1     1     1     1     1     1     1     1     2     2     2     2     2 
-## B46_B B47_B B48_B B49_B B50_B B51_B B52_B B53_B B54_B B55_B B56_B B57_B B58_B 
-##     2     2     2     2     2     2     2     2     2     2     2     2     2 
-## B59_B B60_B B61_B B62_B B63_B B64_B B65_B B66_B B67_B B68_B B69_B B70_B B71_B 
-##     2     2     2     2     2     2     2     2     2     2     2     2     2 
-## B72_B B73_B B74_B B75_B B76_B B77_B B78_B B79_B B80_B 
-##     2     2     2     2     2     2     2     2     2
-
- - - +simp.alp2 <- simp.alp[[1]] +simp.memb <- as.vector(simp.alp2) +names(simp.memb) <- names(simp.alp2) + +simp.memb +
##  A1_A  A2_A  A3_A  A4_A  A5_A  A6_A  A7_A  A8_A  A9_A A10_A A11_A A12_A A13_A 
+##     1     1     1     1     1     1     1     1     1     1     2     2     2 
+## A14_A A15_A A16_A A17_A A18_A A19_A A20_A  B1_B  B2_B  B3_B  B4_B  B5_B  B6_B 
+##     2     2     2     2     2     2     2     1     1     1     1     1     1 
+##  B7_B  B8_B  B9_B B10_B B11_B B12_B B13_B B14_B B15_B B16_B B17_B B18_B B19_B 
+##     1     1     1     1     1     1     1     1     1     1     1     1     1 
+## B20_B B21_B B22_B B23_B B24_B B25_B B26_B B27_B B28_B B29_B B30_B B31_B B32_B 
+##     1     1     1     1     1     1     1     1     1     1     1     1     1 
+## B33_B B34_B B35_B B36_B B37_B B38_B B39_B B40_B B41_B B42_B B43_B B44_B B45_B 
+##     1     1     1     1     1     1     1     1     2     2     2     2     2 
+## B46_B B47_B B48_B B49_B B50_B B51_B B52_B B53_B B54_B B55_B B56_B B57_B B58_B 
+##     2     2     2     2     2     2     2     2     2     2     2     2     2 
+## B59_B B60_B B61_B B62_B B63_B B64_B B65_B B66_B B67_B B68_B B69_B B70_B B71_B 
+##     2     2     2     2     2     2     2     2     2     2     2     2     2 
+## B72_B B73_B B74_B B75_B B76_B B77_B B78_B B79_B B80_B 
+##     2     2     2     2     2     2     2     2     2
+ -
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/ApplicationinGTExData.html b/docs/articles/ApplicationinGTExData.html index 23cbd5e8..ea2adf42 100644 --- a/docs/articles/ApplicationinGTExData.html +++ b/docs/articles/ApplicationinGTExData.html @@ -4,358 +4,494 @@ - + + Building PANDA and LIONESS Regulatory Networks from GTEx Gene Expression Data in R • netZooR - - - + + + - - + - -
-
-
-
-

Building PANDA and LIONESS Regulatory Networks from GTEx Gene Expression Data in R

-

Deborah Weighill

+
+
+

Building PANDA and LIONESS Regulatory Networks from GTEx Gene Expression Data in R

+

Deborah +Weighill

-

2019-11-06

+

2019-11-06

- Source: vignettes/ApplicationinGTExData.Rmd - - + Source: vignettes/ApplicationinGTExData.Rmd +
ApplicationinGTExData.Rmd
-
-

-Introduction

-

In this vignette, we will demonstrate the functionalities of netZooR.

+
+

Introduction +

+

In this vignette, we will demonstrate the functionalities of +netZooR.

-
-

-PANDA

-
-

-PANDA Overview

-

PANDA (Passing Attributes between Networks for Data Assimilation) is a method for constructing gene regulatory networks. It uses message passing to find congruence between 3 different data layers: protein-protein interaction (PPI), gene expression, and transcription factor (TF) motif data.

-

More details can be found in the published paper https://doi.org/10.1371/journal.pone.0064832.

+
+

PANDA +

+
+

PANDA Overview +

+

PANDA (Passing Attributes between Networks for Data Assimilation) is +a method for constructing gene regulatory networks. It uses message +passing to find congruence between 3 different data layers: +protein-protein interaction (PPI), gene expression, and transcription +factor (TF) motif data.

+

More details can be found in the published paper https://doi.org/10.1371/journal.pone.0064832.

-
-

-Running a single PANDA analysis

-

Load some libraries. We use the data.table library for reading in large datasets as it is more efficient.

+
+

Running a single PANDA analysis +

+

Load some libraries. We use the data.table library for reading in +large datasets as it is more efficient.

-library(netZooR)
-library(data.table)
-install.packages("visNetwork",repos = "http://cran.us.r-project.org")
-#> 
-#> The downloaded binary packages are in
-#>  /var/folders/jh/345y9vmx0l7d63r5b05dwp080000gn/T//RtmpHZoDhZ/downloaded_packages
-library(visNetwork) # to visualize the networks
+library(netZooR) +library(data.table) +install.packages("visNetwork",repos = "http://cran.us.r-project.org") +#> +#> The downloaded binary packages are in +#> /var/folders/jh/345y9vmx0l7d63r5b05dwp080000gn/T//Rtmp93EhMW/downloaded_packages +library(visNetwork) # to visualize the networks
-# point R to your python 3 installation. Make sure that this is the installation that has all the required python libraries (numpy, scipy, etc) installed. netZooR uses a python implementation of PANDA under the hood.
-#use_python("/usr/bin/python3") # for example, you can check the installation with py_config()
-

The previous command is necessary to bind R to Python since we are calling PANDA from Python because netZooPy has an optimized implementation of PANDA. Check this tutorial for an example using a pure R implementation of PANDA. Now we locate our ppi and motif priors. The ppi represents physical interactions between transcription factor proteins, and is an undirected network. The motif prior represents putative regulation events where a transcription factor binds in the promotor of a gene to regulate its expression, as predicted by the presence of transcription factor binding motifs in the promotor region of the gene. The motif prior is thus a directed network linking transcription factors to their predicted gene targets. These are small example priors for the purposes of demonstrating this method.

-

The ppi and motif priors are available in our AWS public bucket, and can be downloaded into current working directory.

+# point R to your python 3 installation. Make sure that this is the installation that has all the required python libraries (numpy, scipy, etc) installed. netZooR uses a python implementation of PANDA under the hood. +#use_python("/usr/bin/python3") # for example, you can check the installation with py_config()
+

The previous command is necessary to bind R to Python since we are +calling PANDA from Python because netZooPy has an optimized +implementation of PANDA. Check this +tutorial for an example using a pure R implementation of PANDA. Now +we locate our ppi and motif priors. The ppi represents physical +interactions between transcription factor proteins, and is an undirected +network. The motif prior represents putative regulation events where a +transcription factor binds in the promotor of a gene to regulate its +expression, as predicted by the presence of transcription factor binding +motifs in the promotor region of the gene. The motif prior is thus a +directed network linking transcription factors to their predicted gene +targets. These are small example priors for the purposes of +demonstrating this method.

+

The ppi and motif priors are available in our AWS public bucket, and +can be downloaded into current working directory.

Let’s download and take a look at the priors:

-options(timeout=600) # set timeout for file download
-download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/motif_GTEx.txt","motif_GTEx.txt")
-download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/ppi_GTEx.txt","ppi_GTEx.txt")
-motif <- read.table("./motif_GTEx.txt") 
-ppi <- read.table("./ppi_GTEx.txt")
-ppi[1:5,]
-#>    V1   V2    V3
-#> 1 AHR  AHR 1.000
-#> 2 AHR ALX1 0.179
-#> 3 AHR ALX3 0.179
-#> 4 AHR ALX4 0.179
-#> 5 AHR   AR 0.811
-motif[1:5,]
-#>    V1              V2 V3
-#> 1 AHR ENSG00000004897  1
-#> 2 AHR ENSG00000005339  1
-#> 3 AHR ENSG00000006071  1
-#> 4 AHR ENSG00000006194  1
-#> 5 AHR ENSG00000006283  1
+options(timeout=600) # set timeout for file download +download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/motif_GTEx.txt","motif_GTEx.txt") +download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/ppi_GTEx.txt","ppi_GTEx.txt") +motif <- read.table("./motif_GTEx.txt") +ppi <- read.table("./ppi_GTEx.txt") +ppi[1:5,] +#> V1 V2 V3 +#> 1 AHR AHR 1.000 +#> 2 AHR ALX1 0.179 +#> 3 AHR ALX3 0.179 +#> 4 AHR ALX4 0.179 +#> 5 AHR AR 0.811 +motif[1:5,] +#> V1 V2 V3 +#> 1 AHR ENSG00000004897 1 +#> 2 AHR ENSG00000005339 1 +#> 3 AHR ENSG00000006071 1 +#> 4 AHR ENSG00000006194 1 +#> 5 AHR ENSG00000006283 1

Now we locate out expression data.

-

As example, We will use a portion of the GTEx (Genotype-Tissue Expression) version 7 RNA-Seq data, read in the expression data and the list of LCL samples. Then parse the expression data.

+

As example, We will use a portion of the GTEx (Genotype-Tissue +Expression) version 7 RNA-Seq data, read in the expression data and the +list of LCL samples. Then parse the expression data.

We can either

    -
  1. downlaod the file GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct from from https://gtexportal.org/home/datasets or in our AWS bucket and place it in the folder “expressionData”. We will initially use the LCL RNA-seq data to create a regulatory network for this cell line. Later, we will also generate a regulatory network for whole blood for comaprison.
    2. +
  2. downlaod the file +GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct from from https://gtexportal.org/home/datasets or in our AWS +bucket and place it in the folder “expressionData”. We will initially +use the LCL RNA-seq data to create a regulatory network for this cell +line. Later, we will also generate a regulatory network for whole blood +for comaprison.
-

Here, we use the expression data and sample ids file copy from our AWS bucket.

+

Here, we use the expression data and sample ids file copy from our +AWS bucket.

-download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct","GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct")
-# load the GTEx expression matrix
-expr <- fread("./GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct", header = TRUE, skip = 2, data.table = TRUE)
-# remove the transcript ids so that the genes match the gene ids in the tf-motif prior
-expr$Name<-sub("\\.[0-9]","", expr$Name)
-
-#downlooad and load the sample ids of LCL samples
-download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/LCL_samples.txt","LCL_samples.txt")
-lcl_samples <-fread("./LCL_samples.txt", header = FALSE, data.table=FALSE)
-
-#select the columns of the expression matrix corresponding to the LCL samples.
-lcl_expr <- expr[,union("Name",intersect(c(lcl_samples[1:149,]),colnames(expr))), with=FALSE]
-
-#determine the number of non-NA/non-zero rows in the expression data. This is to be able to ensure that PANDA will have enough values in the vectors to calculate pearson correlations between gene expression profiles in the construction of the gene co-exression prior.
-zero_na_counts <- apply(lcl_expr, MARGIN = 1, FUN = function(x) length(x[(!is.na(x)| x!=0) ]))
-
-#maintain only genes with at least 20 valid gene expression entries
-clean_data <- lcl_expr[zero_na_counts > 20,]
-
-#write the cleaned expression data to a file, ready to be passed as an argument to the PANDA algorithm.
-write.table(clean_data, file = "pandaExprLCL.txt", sep = "\t", col.names = FALSE, row.names = FALSE, quote = FALSE)
+download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct","GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct") +# load the GTEx expression matrix +expr <- fread("./GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct", header = TRUE, skip = 2, data.table = TRUE) +# remove the transcript ids so that the genes match the gene ids in the tf-motif prior +expr$Name<-sub("\\.[0-9]","", expr$Name) + +#downlooad and load the sample ids of LCL samples +download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/LCL_samples.txt","LCL_samples.txt") +lcl_samples <-fread("./LCL_samples.txt", header = FALSE, data.table=FALSE) + +#select the columns of the expression matrix corresponding to the LCL samples. +lcl_expr <- expr[,union("Name",intersect(c(lcl_samples[1:149,]),colnames(expr))), with=FALSE] + +#determine the number of non-NA/non-zero rows in the expression data. This is to be able to ensure that PANDA will have enough values in the vectors to calculate pearson correlations between gene expression profiles in the construction of the gene co-exression prior. +zero_na_counts <- apply(lcl_expr, MARGIN = 1, FUN = function(x) length(x[(!is.na(x)| x!=0) ])) + +#maintain only genes with at least 20 valid gene expression entries +clean_data <- lcl_expr[zero_na_counts > 20,] + +#write the cleaned expression data to a file, ready to be passed as an argument to the PANDA algorithm. +write.table(clean_data, file = "pandaExprLCL.txt", sep = "\t", col.names = FALSE, row.names = FALSE, quote = FALSE)

Or

-

Download our pre-processed pandaExprLCL.txt from our AWS S3 Bucket.

+

Download our pre-processed pandaExprLCL.txt from our AWS S3 +Bucket.

-download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/pandaExprLCL.txt","pandaExprLCL.txt")
-

Now we run PANDA, pointing it to the parsed expression data, motif prior and ppi prior.

+download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/pandaExprLCL.txt","pandaExprLCL.txt")
+

Now we run PANDA, pointing it to the parsed expression data, motif +prior and ppi prior.

-panda_results_LCL <- pandaPy(expr_file = "./pandaExprLCL.txt" , motif_file = "./motif_GTEx.txt", ppi_file = "./ppi_GTEx.txt", modeProcess="legacy", remove_missing = TRUE)
-

Let’s take a look at the results. The output contains a list of three data frames:

+panda_results_LCL <- pandaPy(expr_file = "./pandaExprLCL.txt" , motif_file = "./motif_GTEx.txt", ppi_file = "./ppi_GTEx.txt", modeProcess="legacy", remove_missing = TRUE) +#> Loading motif data ... +#> Elapsed time: 0.05 sec. +#> Loading expression data ... +#> Elapsed time: 0.61 sec. +#> Loading PPI data ... +#> Number of PPIs: 86609 +#> Elapsed time: 0.04 sec. +#> Remove expression not in motif: +#> 55689 rows removed from the initial 56202 +#> Remove motif not in expression data: +#> 58955 rows removed from the initial 103400 +#> Remove ppi not in motif: +#> 0 rows removed from the initial 86609 +#> new case +#> Calculating coexpression network ... +#> Elapsed time: 0.00 sec. +#> Creating motif network ... +#> Elapsed time: 0.04 sec. +#> Creating PPI network ... +#> Elapsed time: 0.10 sec. +#> legacy ./motif_GTEx.txt ./pandaExprLCL.txt ./ppi_GTEx.txt False True False +#> Normalizing networks ... +#> Elapsed time: 0.04 sec. +#> Saving expression matrix and normalized networks ... +#> Elapsed time: 0.02 sec. +#> Running PANDA algorithm ... +#> step: 0, hamming: 0.9678742295275564 +#> step: 1, hamming: 0.398740485692637 +#> step: 2, hamming: 0.2844858236370386 +#> step: 3, hamming: 0.2345843939954457 +#> step: 4, hamming: 0.2042075476717615 +#> step: 5, hamming: 0.18118973861477952 +#> step: 6, hamming: 0.1614704894054021 +#> step: 7, hamming: 0.14360782392336247 +#> step: 8, hamming: 0.12711049339116653 +#> step: 9, hamming: 0.11185402126446312 +#> step: 10, hamming: 0.09783344721350776 +#> step: 11, hamming: 0.08507016957437544 +#> step: 12, hamming: 0.07356780340154848 +#> step: 13, hamming: 0.06330116711146466 +#> step: 14, hamming: 0.05421852200951187 +#> step: 15, hamming: 0.04624764701825898 +#> step: 16, hamming: 0.039301895859622696 +#> step: 17, hamming: 0.03328718090872966 +#> step: 18, hamming: 0.028107462583496584 +#> step: 19, hamming: 0.023668722559827687 +#> step: 20, hamming: 0.01988152451165294 +#> step: 21, hamming: 0.016662707998748628 +#> step: 22, hamming: 0.013936416334063792 +#> step: 23, hamming: 0.011634447928678703 +#> step: 24, hamming: 0.009696184613626216 +#> step: 25, hamming: 0.00806828212583198 +#> step: 26, hamming: 0.006704156831568278 +#> step: 27, hamming: 0.005563429715892799 +#> step: 28, hamming: 0.004611309821200676 +#> step: 29, hamming: 0.003817972519694832 +#> step: 30, hamming: 0.0031579704664075493 +#> step: 31, hamming: 0.0026096760497343573 +#> step: 32, hamming: 0.002154774682668182 +#> step: 33, hamming: 0.0017778073470498862 +#> step: 34, hamming: 0.0014657623367399985 +#> step: 35, hamming: 0.0012077153464268792 +#> step: 36, hamming: 0.0009945161790090437 +#> Running panda took: 1.31 seconds!
+

Let’s take a look at the results. The output contains a list of three +data frames:

    -
  • data frame containing the regulatory network (bipartite graph) with edge weights representing the “likelihood” that a transcription factor binds the promotor of and regulates the expression of a gene.
    +
  • data frame containing the regulatory network (bipartite graph) with +edge weights representing the “likelihood” that a transcription factor +binds the promotor of and regulates the expression of a gene.
    • -
  • data frame odf the in-degrees of genes (sum of the weights of inbound edges around a gene)
    • -
  • data frame of the out-degrees of TFs (sum of the weights of outbound edges around a TF)
    • +
  • data frame odf the in-degrees of genes (sum of the weights of +inbound edges around a gene)
    • +
  • data frame of the out-degrees of TFs (sum of the weights of outbound +edges around a TF)
-# the bipartite regulatory network
-regNetLCL <- panda_results_LCL$panda
-regNetLCL[1:5,]
-#>     TF            Gene Motif       Score
-#> 1  AHR ENSG00000049245     0 -0.66273059
-#> 2 AIRE ENSG00000049245     1  3.91520334
-#> 3 ALX1 ENSG00000049245     0  0.04430445
-#> 4 ALX3 ENSG00000049245     0  0.09652412
-#> 5 ALX4 ENSG00000049245     0 -0.06595998
-
-# gene in-degree
-inDegreeLCL <- panda_results_LCL$indegree
-head(inDegreeLCL)
-#>            Target Target_Score
-#> 1 ENSG00000000005    -47.74480
-#> 2 ENSG00000000419    -11.80864
-#> 3 ENSG00000000457     38.85765
-#> 4 ENSG00000000938     30.19761
-#> 5 ENSG00000001036    -56.41923
-#> 6 ENSG00000001084    292.77857
-
-# TF out-degree
-outDegreeLCL <- panda_results_LCL$outdegree
-head(outDegreeLCL)
-#>   Regulator Regulator_Score
-#> 1       AHR      -172.47026
-#> 2      AIRE       115.74408
-#> 3      ALX1        83.45379
-#> 4      ALX3       139.81888
-#> 5      ALX4       119.41043
-#> 6        AR      -146.19404
+# the bipartite regulatory network +regNetLCL <- panda_results_LCL$panda +regNetLCL[1:5,] +#> TF Gene Motif Score +#> 1 AHR ENSG00000049245 0 -0.66273059 +#> 2 AIRE ENSG00000049245 1 3.91520334 +#> 3 ALX1 ENSG00000049245 0 0.04430445 +#> 4 ALX3 ENSG00000049245 0 0.09652412 +#> 5 ALX4 ENSG00000049245 0 -0.06595998 + +# gene in-degree +inDegreeLCL <- panda_results_LCL$indegree +head(inDegreeLCL) +#> Target Target_Score +#> 1 ENSG00000000005 -47.74480 +#> 2 ENSG00000000419 -11.80864 +#> 3 ENSG00000000457 38.85765 +#> 4 ENSG00000000938 30.19761 +#> 5 ENSG00000001036 -56.41923 +#> 6 ENSG00000001084 292.77857 + +# TF out-degree +outDegreeLCL <- panda_results_LCL$outdegree +head(outDegreeLCL) +#> Regulator Regulator_Score +#> 1 AHR -172.47026 +#> 2 AIRE 115.74408 +#> 3 ALX1 83.45379 +#> 4 ALX3 139.81888 +#> 5 ALX4 119.41043 +#> 6 AR -146.19404
-
-

-Run another PANDA analysis on Whole Blood Samples

-

Like the LCL expression data in previous section, we can either download the raw data and process;

+
+

Run another PANDA analysis on Whole Blood Samples +

+

Like the LCL expression data in previous section, we can either +download the raw data and process;

-#### skip this part if you already did same process in LCL expression data section
-download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct","GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct")
-# load the GTEx expression matrix
-expr <- fread("./GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct", header = TRUE, skip = 2, data.table = TRUE)
-# remove the transcript ids so that the genes match the gene ids in the tf-motif prior
-expr$Name<-sub("\\.[0-9]","", expr$Name)
-#####
-
-#load the sample ids of Whole Blood samples
-download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/WholeBlood_samples.txt","WholeBlood_samples.txt")
-wblood_samples <-fread("./WholeBlood_samples.txt", header = FALSE, data.table=FALSE)
-
-#select the columns of the expression matrix corresponding to the LCL samples.
-wblood_expr <- expr[,union("Name",intersect(c(wblood_samples[1:149,]),colnames(expr))), with=FALSE]
-
-#determine the number of non-NA/non-zero rows in the expression data. This is to be able to ensure that PANDA will have enough values in the vectors to calculate pearson correlations between gene expression profiles in the construction of the gene co-exression prior.
-zero_na_counts_wblood <- apply(wblood_expr, MARGIN = 1, FUN = function(x) length(x[(!is.na(x)| x!=0) ]))
-
-#maintain only genes with at least 20 valid gene expression entries
-clean_data_wb <- wblood_expr[zero_na_counts_wblood > 20,]
-
-#write the cleaned expression data to a file, ready to be passed as an argument to the PANDA algorithm.
-write.table(clean_data_wb, file = "pandaExprWholeBlood.txt", sep = "\t", col.names = FALSE, row.names = FALSE, quote = FALSE)
-

or download the whole blood expression data directly from AWS Bucket.

+#### skip this part if you already did same process in LCL expression data section +download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct","GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct") +# load the GTEx expression matrix +expr <- fread("./GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct", header = TRUE, skip = 2, data.table = TRUE) +# remove the transcript ids so that the genes match the gene ids in the tf-motif prior +expr$Name<-sub("\\.[0-9]","", expr$Name) +##### + +#load the sample ids of Whole Blood samples +download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/WholeBlood_samples.txt","WholeBlood_samples.txt") +wblood_samples <-fread("./WholeBlood_samples.txt", header = FALSE, data.table=FALSE) + +#select the columns of the expression matrix corresponding to the LCL samples. +wblood_expr <- expr[,union("Name",intersect(c(wblood_samples[1:149,]),colnames(expr))), with=FALSE] + +#determine the number of non-NA/non-zero rows in the expression data. This is to be able to ensure that PANDA will have enough values in the vectors to calculate pearson correlations between gene expression profiles in the construction of the gene co-exression prior. +zero_na_counts_wblood <- apply(wblood_expr, MARGIN = 1, FUN = function(x) length(x[(!is.na(x)| x!=0) ])) + +#maintain only genes with at least 20 valid gene expression entries +clean_data_wb <- wblood_expr[zero_na_counts_wblood > 20,] + +#write the cleaned expression data to a file, ready to be passed as an argument to the PANDA algorithm. +write.table(clean_data_wb, file = "pandaExprWholeBlood.txt", sep = "\t", col.names = FALSE, row.names = FALSE, quote = FALSE)
+

or download the whole blood expression data directly from AWS +Bucket.

-download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/pandaExprWholeBlood.txt","pandaExprWholeBlood.txt")
+download.file("https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/pandaExprWholeBlood.txt","pandaExprWholeBlood.txt")
-#run PANDA
-panda_results_wblood <- pandaPy(expr_file = "./pandaExprWholeBlood.txt" , motif_file = "./motif_GTEx.txt", ppi_file = "./ppi_GTEx.txt", modeProcess="legacy", remove_missing = TRUE)
-#> Use the legacy mode to pre-process the input dataset and keep only the matched TFs or Genes
-#> ...Finish PANDA...
+#run PANDA +panda_results_wblood <- pandaPy(expr_file = "./pandaExprWholeBlood.txt" , motif_file = "./motif_GTEx.txt", ppi_file = "./ppi_GTEx.txt", modeProcess="legacy", remove_missing = TRUE) +#> Use the legacy mode to pre-process the input dataset and keep only the matched TFs or Genes +#> Loading motif data ... +#> Elapsed time: 0.05 sec. +#> Loading expression data ... +#> Elapsed time: 0.24 sec. +#> Loading PPI data ... +#> Number of PPIs: 86609 +#> Elapsed time: 0.07 sec. +#> Remove expression not in motif: +#> 55689 rows removed from the initial 56202 +#> Remove motif not in expression data: +#> 58955 rows removed from the initial 103400 +#> Remove ppi not in motif: +#> 0 rows removed from the initial 86609 +#> new case +#> Calculating coexpression network ... +#> Elapsed time: 0.01 sec. +#> Creating motif network ... +#> Elapsed time: 0.04 sec. +#> Creating PPI network ... +#> Elapsed time: 0.10 sec. +#> legacy ./motif_GTEx.txt ./pandaExprWholeBlood.txt ./ppi_GTEx.txt False True False +#> Normalizing networks ... +#> Elapsed time: 0.04 sec. +#> Saving expression matrix and normalized networks ... +#> Elapsed time: 0.01 sec. +#> Running PANDA algorithm ... +#> step: 0, hamming: 1.0690849805252594 +#> step: 1, hamming: 0.4691397610863155 +#> step: 2, hamming: 0.33036461786634547 +#> step: 3, hamming: 0.26806926123263264 +#> step: 4, hamming: 0.23042570772644422 +#> step: 5, hamming: 0.20256536152502197 +#> step: 6, hamming: 0.17929462709712923 +#> step: 7, hamming: 0.15865735441197737 +#> step: 8, hamming: 0.13989696240746843 +#> step: 9, hamming: 0.12274921075135972 +#> step: 10, hamming: 0.10712627123983814 +#> step: 11, hamming: 0.09299263928203307 +#> step: 12, hamming: 0.08031383413612993 +#> step: 13, hamming: 0.06903610891148329 +#> step: 14, hamming: 0.05908483808770143 +#> step: 15, hamming: 0.050368570841309156 +#> step: 16, hamming: 0.04278429695791332 +#> step: 17, hamming: 0.036223979927264414 +#> step: 18, hamming: 0.030579252521705833 +#> step: 19, hamming: 0.025745150525848713 +#> step: 20, hamming: 0.02162261509032571 +#> step: 21, hamming: 0.018120119978282237 +#> step: 22, hamming: 0.015154398422204283 +#> step: 23, hamming: 0.012650788145759574 +#> step: 24, hamming: 0.010543041908389053 +#> step: 25, hamming: 0.008772952126201682 +#> step: 26, hamming: 0.007289762109077401 +#> step: 27, hamming: 0.006049506006422676 +#> step: 28, hamming: 0.005014322747086903 +#> step: 29, hamming: 0.004151769096928693 +#> step: 30, hamming: 0.0034341712803001153 +#> step: 31, hamming: 0.002838014367091253 +#> step: 32, hamming: 0.0023433884769824887 +#> step: 33, hamming: 0.0019334886365366026 +#> step: 34, hamming: 0.0015941705108742312 +#> step: 35, hamming: 0.001313559430530958 +#> step: 36, hamming: 0.0010817088184296897 +#> step: 37, hamming: 0.0008903048282875378 +#> Running panda took: 1.51 seconds! +#> ...Finish PANDA...
-install.packages("visNetwork",repos = "http://cran.us.r-project.org",dependencies=TRUE)
-library(visNetwork)
-edges <- head(panda_results_wblood$panda[order(panda_results_wblood$panda$Score,decreasing = TRUE),], 500)
-colnames(edges) <- c("from","to","motif","force")
-nodes <- data.frame(id = unique(as.vector(as.matrix(edges[,c(1,2)]))))
-nodes$group <- ifelse(nodes$id %in% edges$from, "TF", "gene")
-
-net <- visNetwork(nodes, edges, width = "100%")
-net <- visGroups(net, groupname = "TF", shape = "square",
-                     color = list(background = "teal", border="black"))
-net <- visGroups(net, groupname = "gene", shape = "dot",       
-                     color = list(background = "gold", border="black"))
-visLegend(net, main="Legend", position="right", ncol=1)
+install.packages("visNetwork",repos = "http://cran.us.r-project.org",dependencies=TRUE) +library(visNetwork) +edges <- head(panda_results_wblood$panda[order(panda_results_wblood$panda$Score,decreasing = TRUE),], 500) +colnames(edges) <- c("from","to","motif","force") +nodes <- data.frame(id = unique(as.vector(as.matrix(edges[,c(1,2)])))) +nodes$group <- ifelse(nodes$id %in% edges$from, "TF", "gene") + +net <- visNetwork(nodes, edges, width = "100%") +net <- visGroups(net, groupname = "TF", shape = "square", + color = list(background = "teal", border="black")) +net <- visGroups(net, groupname = "gene", shape = "dot", + color = list(background = "gold", border="black")) +visLegend(net, main="Legend", position="right", ncol=1)
-
-

-LIONESS

-

LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) is a method for creating sample-specific networks. When applied to a PANDA regulatory network, the result is a set of gene regulatory networks, one for each sample in the gene expression dataset. More information on LIONESS can be found in the published paper: https://doi.org/10.1016/j.isci.2019.03.021

-

Running LIONESS with netZoo is simple, and very similar to running PANDA:

+
+

LIONESS +

+

LIONESS (Linear Interpolation to Obtain Network Estimates for Single +Samples) is a method for creating sample-specific networks. When applied +to a PANDA regulatory network, the result is a set of gene regulatory +networks, one for each sample in the gene expression dataset. More +information on LIONESS can be found in the published paper: https://doi.org/10.1016/j.isci.2019.03.021

+

Running LIONESS with netZoo is simple, and very similar to running +PANDA:

-lionessLCL <- lionessPy(expr_file = "pandaExprLCL.txt" , motif_file = "./motif_GTEx.txt", ppi_file = "./ppi_GTEx.txt", modeProcess="legacy", remove_missing = TRUE)
-#> Use the legacy mode to pre-process the input dataset and keep only the matched TFs or Genes
-

The result is a data frame in which the first colum contains TFs, the second column contains genes and each subsequent column contains the edge weight for that particular TF-gene pair in a particular sample.

+lionessLCL <- lionessPy(expr_file = "pandaExprLCL.txt" , motif_file = "./motif_GTEx.txt", ppi_file = "./ppi_GTEx.txt", modeProcess="legacy", remove_missing = TRUE) +#> Use the legacy mode to pre-process the input dataset and keep only the matched TFs or Genes
+

The result is a data frame in which the first colum contains TFs, the +second column contains genes and each subsequent column contains the +edge weight for that particular TF-gene pair in a particular sample.

-lionessLCL[1:5,1:10]
-#>     tf            gene          0          1          2          3        4
-#> 1  AHR ENSG00000049245 0.06820364   -1.01318 -0.1190873 -0.6471435 -4.72954
-#> 2 AIRE ENSG00000049245   3.405967   3.606517   1.341022   3.962872 10.14026
-#> 3 ALX1 ENSG00000049245  -1.564644 -0.6531037  -2.583472 0.06944336 1.548766
-#> 4 ALX3 ENSG00000049245  -2.265761  0.4374339  -1.842543  0.1693952 7.957916
-#> 5 ALX4 ENSG00000049245  -2.300186  0.1123922   -1.83354 0.01588022 5.195771
-#>            5         6          7
-#> 1 -0.1790814 -0.546194 -0.9628234
-#> 2   2.654841  4.577901   3.730394
-#> 3 -0.6345625 0.8294741   2.063451
-#> 4 -0.4487257 0.3399528   1.848871
-#> 5 -0.3720986 0.1874633   2.238461
-
- - - - + -
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/ApplicationwithTBdataset.html b/docs/articles/ApplicationwithTBdataset.html index 3b0e8457..b3a8255b 100644 --- a/docs/articles/ApplicationwithTBdataset.html +++ b/docs/articles/ApplicationwithTBdataset.html @@ -4,516 +4,564 @@ - + + netZooR application with TB dataset • netZooR - - - + + + - - + - -
-
-
-
-

netZooR application with TB dataset

-

Tian Wang

+ + +
+
+

netZooR application with TB dataset

+

Tian Wang

-

2019-10-16

+

2019-10-16

- Source: vignettes/ApplicationwithTBdataset.Rmd - - + Source: vignettes/ApplicationwithTBdataset.Rmd +
ApplicationwithTBdataset.Rmd
-
-

-Introduction

-

netZooR is an R package which consists of seven main algorithms and is able to construct, analyse and plot gene regulatory networks.

+
+

Introduction +

+

netZooR is an R package which consists of seven main algorithms and +is able to construct, analyse and plot gene regulatory networks.

    -

  • PANDA(Passing Attributes between Networks for Data Assimilation) is a message-passing model to gene regulatory network reconstruction. It integrates multiple sources of biological data, including protein-protein interaction, gene expression, and transcription factor binding motifs information to reconstruct genome-wide, condition-specific regulatory networks.[Glass et al. 2013]

    • -

  • LIONESS(Linear Interpolation to Obtain Network Estimates for Single Samples) is a method to estimate sample-specific regulatory networks by applying linear interpolation to the predictions made by existing aggregate network inference approaches.[[Kuijjer et al. 2019]]](https://www.sciencedirect.com/science/article/pii/S2589004219300872)

    • -

  • CONDOR (COmplex Network Description Of Regulators) implements methods to cluster biapartite networks and estimatiing the contribution of each node to its community’s modularity.[Platig et al. 2016]

    • -

  • ALPACA(ALtered Partitions Across Community Architectures) is a method to compare two genome-scale networks derived from different phenotypic states to identify condition-specific modules. [Padi and Quackenbush 2018]

    • -

  • SAMBAR(Subtyping Agglomerated Mutations By Annotation Relations) is a method to identify subtypes based on somatic mutation data.[Kuijjer et al.].

    • -

  • MONSTER(Modeling Network State Transitions from Expression and Regulatory data)[Schlauch et al.]: infers transcription factor which drivers of cell state conditions at the gene regulatory network level.

    • -

  • OTTER(Optimization to Estimate Regulation) [publication in preparation]: models gene regulation estimation as a graph matrching problem

    • +

  • PANDA(Passing Attributes between Networks for +Data Assimilation) is a message-passing model to gene regulatory network +reconstruction. It integrates multiple sources of biological data, +including protein-protein interaction, gene expression, and +transcription factor binding motifs information to reconstruct +genome-wide, condition-specific regulatory networks.[Glass +et al. 2013]

    • +

  • LIONESS(Linear Interpolation to Obtain Network +Estimates for Single Samples) is a method to estimate sample-specific +regulatory networks by applying linear interpolation to the predictions +made by existing aggregate network inference approaches.[[Kuijjer et +al. 2019]]](https://www.sciencedirect.com/science/article/pii/S2589004219300872)

    • +

  • CONDOR (COmplex Network Description Of +Regulators) implements methods to cluster biapartite networks and +estimatiing the contribution of each node to its community’s +modularity.[Platig +et al. 2016]

    • +

  • ALPACA(ALtered Partitions Across Community +Architectures) is a method to compare two genome-scale networks derived +from different phenotypic states to identify condition-specific modules. +[Padi and +Quackenbush 2018]

    • +

  • SAMBAR(Subtyping Agglomerated Mutations By +Annotation Relations) is a method to identify subtypes based on somatic +mutation data.[Kuijjer et +al.].

    • +

  • MONSTER(Modeling Network State Transitions from +Expression and Regulatory data)[Schlauch et al.]: +infers transcription factor which drivers of cell state conditions at +the gene regulatory network level.

    • +

  • OTTER(Optimization to Estimate Regulation) +[publication in preparation]: models gene regulation estimation as a +graph matrching problem

-
-

-Installation

-
-

-Prerequisites

-

Using this pacakage requires Python (3.X) and some Python libraries, R (>= 3.3.3), and stable Internet access.

-

Some plotting functions will require the Cytoscape installed.

+
+

Installation +

+
+

Prerequisites +

+

Using this pacakage requires Python +(3.X) and some Python +libraries, R (>= 3.3.3), +and stable Internet access.

+

Some plotting functions will require the Cytoscape +installed.

-
-

-Required Python libraries

-

How to install Python libraries depends varies from different platforms. More instructions could be find here.

-

The following Python libraries (or packages) are required by running PANDA and LIONESS algorithms:

-

The required Python packages are: pandas, numpy, networkx, matplotlib.pyplot.

+
+

Required Python libraries +

+

How to install Python libraries depends varies from different +platforms. More instructions could be find here.

+

The following Python libraries (or packages) are required by running +PANDA and LIONESS algorithms:

+

The required Python packages are: pandas, numpy, networkx, matplotlib.pyplot.

-
-

-Installing

-

This package could be downloaded via install_github() function from devtools package.

+
+

Installing +

+

This package could be downloaded via install_github() +function from devtools package.

-# install.packages("devtools") 
-library(devtools)
-# install netZooR pkg with vignettes, otherwise remove the "build_vignettes = TRUE" argument.
-devtools::install_github("netZoo/netZooR", build_vignettes = TRUE)
-library(viridisLite)#To visualize communities
+# install.packages("devtools") +library(devtools) +# install netZooR pkg with vignettes, otherwise remove the "build_vignettes = TRUE" argument. +devtools::install_github("netZoo/netZooR", build_vignettes = TRUE) +library(viridisLite)#To visualize communities
-
-

-Data Resources

-
-

-Motif data

-

Here is some pre-prepared specie-sepcific PANDA-ready transcription factor binding motifs data stored in our AWS bucket https://s3.console.aws.amazon.com/s3/buckets/netzoo/netZooR/example_datasets/PANDA_ready_motif_prior/?region=us-east-2&tab=overview, which are derived from motif scan and motif info files located on https://sites.google.com/a/channing.harvard.edu/kimberlyglass/tools/resourcesby .

+
+

Data Resources +

+
+

Motif data +

+

Here is some pre-prepared specie-sepcific PANDA-ready transcription +factor binding motifs data stored in our AWS bucket https://s3.console.aws.amazon.com/s3/buckets/netzoo/netZooR/example_datasets/PANDA_ready_motif_prior/?region=us-east-2&tab=overview, +which are derived from motif scan and motif info files located on https://sites.google.com/a/channing.harvard.edu/kimberlyglass/tools/resourcesby +.

-
-

-PPI

-

This package includes a function source.PPI may source a Protein-Protein Interactions (PPI) througt STRING database given a list of proteins of interest. The STRINGdb is already loaded while loading netZooR.

+
+

PPI +

+

This package includes a function source.PPI may source a +Protein-Protein Interactions (PPI) througt STRING database given a list +of proteins of interest. The STRINGdb +is already loaded while loading netZooR.

-# TF is a data frame with single column filled with TFs of Mycobacterium tuberculosis H37Rv.
-PPI <- source.PPI(TF, STRING.version="10", species.index=83332, score_threshold=0)
+# TF is a data frame with single column filled with TFs of Mycobacterium tuberculosis H37Rv. +PPI <- source.PPI(TF, STRING.version="10", species.index=83332, score_threshold=0)
-
-

-Running the sample TB datasets

+
+

Running the sample TB datasets +

-library(netZooR)
-#> Loading required package: igraph
-#> 
-#> Attaching package: 'igraph'
-#> The following objects are masked from 'package:stats':
-#> 
-#>     decompose, spectrum
-#> The following object is masked from 'package:base':
-#> 
-#>     union
-#> Loading required package: reticulate
-#> Loading required package: pandaR
-#> Loading required package: Biobase
-#> Loading required package: BiocGenerics
-#> Loading required package: parallel
-#> 
-#> Attaching package: 'BiocGenerics'
-#> The following objects are masked from 'package:parallel':
-#> 
-#>     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
-#>     clusterExport, clusterMap, parApply, parCapply, parLapply,
-#>     parLapplyLB, parRapply, parSapply, parSapplyLB
-#> The following objects are masked from 'package:igraph':
-#> 
-#>     normalize, path, union
-#> The following objects are masked from 'package:stats':
-#> 
-#>     IQR, mad, sd, var, xtabs
-#> The following objects are masked from 'package:base':
-#> 
-#>     anyDuplicated, append, as.data.frame, basename, cbind, colnames,
-#>     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
-#>     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
-#>     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
-#>     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
-#>     union, unique, unsplit, which.max, which.min
-#> Welcome to Bioconductor
-#> 
-#>     Vignettes contain introductory material; view with
-#>     'browseVignettes()'. To cite Bioconductor, see
-#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
-#> Loading required package: yarn
-#> Setting options('download.file.method.GEOquery'='auto')
-#> Setting options('GEOquery.inmemory.gpl'=FALSE)
-#> 
-#>
+library(netZooR) +#> Loading required package: igraph +#> +#> Attaching package: 'igraph' +#> The following objects are masked from 'package:stats': +#> +#> decompose, spectrum +#> The following object is masked from 'package:base': +#> +#> union +#> Loading required package: reticulate +#> Loading required package: pandaR +#> Loading required package: Biobase +#> Loading required package: BiocGenerics +#> +#> Attaching package: 'BiocGenerics' +#> The following objects are masked from 'package:igraph': +#> +#> normalize, path, union +#> The following objects are masked from 'package:stats': +#> +#> IQR, mad, sd, var, xtabs +#> The following objects are masked from 'package:base': +#> +#> anyDuplicated, aperm, append, as.data.frame, basename, cbind, +#> colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, +#> get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, +#> match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, +#> Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, +#> table, tapply, union, unique, unsplit, which.max, which.min +#> Welcome to Bioconductor +#> +#> Vignettes contain introductory material; view with +#> 'browseVignettes()'. To cite Bioconductor, see +#> 'citation("Biobase")', and for packages 'citation("pkgname")'. +#> Loading required package: yarn +#> Setting options('download.file.method.GEOquery'='auto') +#> Setting options('GEOquery.inmemory.gpl'=FALSE) +#> Loading required package: matrixcalc +#> +#> Attaching package: 'matrixcalc' +#> The following object is masked from 'package:igraph': +#> +#> %s% +#> +#>

Accessing the help pages for the usage of core functions.

-?pandaPy
-?createCondorObject
-?pandaToCondorObject
-?lionessPy
-?alpaca
-?pandaToAlpaca
-?sambar
-

This package will invoke the Python in R environment through reticulate package. Configure which version of Python to use if necessary, here in netZooR, Python 3.X is required. More details can be found here

+?pandaPy +?createCondorObject +?pandaToCondorObject +?lionessPy +?alpaca +?pandaToAlpaca +?sambar
+

This package will invoke the Python in R environment through +reticulate package. Configure which version of Python to use if +necessary, here in netZooR, Python 3.X is required. More details can be +found here

-#check your Python configuration and the specific version of Python in use currently
-py_config()
-
-# reset to Python 3.X if necessary, like below:
-use_python("/usr/local/bin/python3")
-

The previous command is necessary to bind R to Python since we are calling PANDA from Python because netZooPy has an optimized implementation of PANDA. Check this tutorial for an example using a pure R implementation of PANDA. Use example data sets within package to test this package. Refer to four input datasets files: one TB expression dataset control group , one TB expression dataset treated, one transcription factor binding motifs dataset, and one protein-protein interaction datasets from either inst/extdat or AWS.

-

retrieve the file path of these files came with the netZooR package.

+#check your Python configuration and the specific version of Python in use currently +py_config() + +# reset to Python 3.X if necessary, like below: +use_python("/usr/local/bin/python3")
+

The previous command is necessary to bind R to Python since we are +calling PANDA from Python because netZooPy has an optimized +implementation of PANDA. Check this +tutorial for an example using a pure R implementation of PANDA. Use +example data sets within package to test this package. Refer to four +input datasets files: one TB expression dataset control group , one TB +expression dataset treated, one transcription factor binding motifs +dataset, and one protein-protein interaction datasets from either +inst/extdat or AWS.

+

retrieve the file path of these files came with the netZooR +package.

-# retrieve the file path of these files
-treated_expression_file_path <- system.file("extdata", "expr4_matched.txt", package = "netZooR", mustWork = TRUE)
-control_expression_file_path <- system.file("extdata", "expr10_matched.txt", package = "netZooR", mustWork = TRUE)
-motif_file_path <- system.file("extdata", "chip_matched.txt", package = "netZooR", mustWork = TRUE)
-ppi_file_path <- system.file("extdata", "ppi_matched.txt", package = "netZooR", mustWork = TRUE)
-
-

-PANDA algorithm

-

Assign the above file paths to flag e(refers to “expression dataset”), m(refers to “motif dataset”), and ppi(refers to “PPI” dataset), respectively. Then set option rm_missing to TRUE to run PANDA to generate an aggregate network without unmatched TF and genes.

+# retrieve the file path of these files +treated_expression_file_path <- system.file("extdata", "expr4_matched.txt", package = "netZooR", mustWork = TRUE) +control_expression_file_path <- system.file("extdata", "expr10_matched.txt", package = "netZooR", mustWork = TRUE) +motif_file_path <- system.file("extdata", "chip_matched.txt", package = "netZooR", mustWork = TRUE) +ppi_file_path <- system.file("extdata", "ppi_matched.txt", package = "netZooR", mustWork = TRUE)
+
+

PANDA algorithm +

+

Assign the above file paths to flag e(refers to +“expression dataset”), m(refers to “motif dataset”), and +ppi(refers to “PPI” dataset), respectively. Then set option +rm_missing to TRUE to run +PANDA to generate an aggregate network without +unmatched TF and genes.

Repeat with control group.

-treated_all_panda_result <- pandaPy(expr_file = treated_expression_file_path, motif_file = motif_file_path, ppi_file= ppi_file_path,modeProcess="legacy",  remove_missing = TRUE )
-control_all_panda_result <- pandaPy(expr_file = control_expression_file_path,motif_file = motif_file_path, ppi_file= ppi_file_path,modeProcess="legacy",  remove_missing = TRUE )
-

Vector treated_all_panda_result and vector control_all_panda_result below are large lists with three elements: the entire PANDA network, indegree (“to” nodes) nodes and score, outdegree (“from” nodes) nodes and score. Use $panda,$indegree and $outdegree to access each list item resepctively.

+treated_all_panda_result <- pandaPy(expr_file = treated_expression_file_path, motif_file = motif_file_path, ppi_file= ppi_file_path,modeProcess="legacy", remove_missing = TRUE ) +control_all_panda_result <- pandaPy(expr_file = control_expression_file_path,motif_file = motif_file_path, ppi_file= ppi_file_path,modeProcess="legacy", remove_missing = TRUE )
+

Vector treated_all_panda_result and vector +control_all_panda_result below are large lists with three +elements: the entire PANDA network, indegree (“to” nodes) nodes and +score, outdegree (“from” nodes) nodes and score. Use +$panda,$indegree and $outdegree +to access each list item resepctively.

Use $pandato access the entire PANDA network.

-treated_net <- treated_all_panda_result$panda
-control_net <- control_all_panda_result$panda
+treated_net <- treated_all_panda_result$panda +control_net <- control_all_panda_result$panda
-
-

-PANDA Cytoscape Plotting

-

Cytoscape is an interactivity network visualization tool highly recommanded to explore the PANDA network. Before using this function plot.panda.in.cytoscape, please install and launch Cytoscape (3.6.1 or greater) and keep it running whenever using.

+
+

PANDA Cytoscape Plotting +

+

Cytoscape is an interactivity network visualization tool highly +recommanded to explore the PANDA network. Before using this function +plot.panda.in.cytoscape, please install and launch +Cytoscape (3.6.1 or greater) and keep it running whenever using.

-# select top 1000 edges in PANDA network by edge weight.
-panda.net <- head(treated_net[order(control_net$force,decreasing = TRUE),], 1000)
- 
-# run this function to create a network in Cytoscape.
-vis.panda.in.cytoscape(panda.net, network.name="PANDA")
+# select top 1000 edges in PANDA network by edge weight. +panda.net <- head(treated_net[order(control_net$force,decreasing = TRUE),], 1000) + +# run this function to create a network in Cytoscape. +vis.panda.in.cytoscape(panda.net, network.name="PANDA")
-
-

-LIONESS Algorithm

-

How to run LIONESS is mostly idential with method how to run PANDA in this package, unless the return values of lionessPy() is a data frame where first two columns represent TFs (regulators) and Genes (targets) while the rest columns represent each sample. each cell filled with estimated score calculated by LIONESS.

+
+

LIONESS Algorithm +

+

How to run LIONESS is mostly idential with method how to run PANDA in +this package, unless the return values of lionessPy() is a +data frame where first two columns represent TFs (regulators) and Genes +(targets) while the rest columns represent each sample. each cell filled +with estimated score calculated by LIONESS.

-# Run LIONESS algorithm for the first two samples
-# removing start_sample and end_sample arguments to generate whole LIONESS network with all samples.
-control_lioness_result <- lionessPy(expr_file = control_expression_file_path,motif_file = motif_file_path, ppi_file= ppi_file_path,modeProcess="legacy",  remove_missing = TRUE, start_sample=1, end_sample=2)
+# Run LIONESS algorithm for the first two samples +# removing start_sample and end_sample arguments to generate whole LIONESS network with all samples. +control_lioness_result <- lionessPy(expr_file = control_expression_file_path,motif_file = motif_file_path, ppi_file= ppi_file_path,modeProcess="legacy", remove_missing = TRUE, start_sample=1, end_sample=2)
-
-

-CONDOR Algorithm and plotting

-

PANDA network can simply be converted into condor.object by pandaToCondorObject(panda.net, threshold) Defaults option threshold is the average of [median weight of non-prior edges] and [median weight of prior edges], all weights mentioned previous are transformationed with formula w'=ln(e^w+1) before calculating the median and average. But all the edges selected will remain the orginal weights calculated by PANDA.

+
+

CONDOR Algorithm and plotting +

+

PANDA network can simply be converted into condor.object by +pandaToCondorObject(panda.net, threshold) Defaults option +threshold is the average of [median weight of non-prior +edges] and [median weight of prior edges], all weights mentioned +previous are transformationed with formula w'=ln(e^w+1) +before calculating the median and average. But all the edges selected +will remain the orginal weights calculated by PANDA.

-treated_condor_object <- pandaToCondorObject(treated_net, threshold = 0)
+treated_condor_object <- pandaToCondorObject(treated_net, threshold = 0)

The communities structure can be plotted by igraph.

-library(viridisLite)
-treated_condor_object <-condorCluster(treated_condor_object,project = FALSE)
-#> [1] "modularity of projected graph 0.306134881290028"
-#> [1] "Q = 0.306345744728624"
-#> [1] "Q = 0.306345744728624"
-treated_color_num <- max(treated_condor_object$red.memb$com)
-treated_color <- viridis(treated_color_num, alpha = 1, begin = 0, end = 1, direction = 1, option = "D")
-condorPlotCommunities(treated_condor_object, color_list=treated_color, point.size=0.04, xlab="Genes", ylab="TFs")
+library(viridisLite) +treated_condor_object <-condorCluster(treated_condor_object,project = FALSE) +#> [1] "modularity of projected graph 0.308479519056006" +#> [1] "Q = 0.308566500564539" +#> [1] "Q = 0.308658883596709" +#> [1] "Q = 0.308658883596709" +treated_color_num <- max(treated_condor_object$red.memb$com) +treated_color <- viridis(treated_color_num, alpha = 1, begin = 0, end = 1, direction = 1, option = "D") +condorPlotCommunities(treated_condor_object, color_list=treated_color, point.size=0.04, xlab="Genes", ylab="TFs")

-
-

-ALPACA Algorithm

-

ALPACA community structure can also be generated from two PANDA network by pandaToAlpaca

+
+

ALPACA Algorithm +

+

ALPACA community structure can also be generated from two PANDA +network by pandaToAlpaca

-alpaca<- pandaToAlpaca(treated_net, control_net, NULL, verbose=FALSE)
-#> [1] "Detecting communities in control network..."
-#> Weights detected. Building condor object with weighted edges.
-#> [1] "modularity of projected graph 0.482467704493567"
-#> [1] "Q = 0.49405163585898"
-#> [1] "Q = 0.49405163585898"
-#> [1] "Computing differential modularity matrix..."
-#> [1] "Computing differential modules..."
-#> [1] "Merging 2644 communities"
-#> [1] 1
-#> [1] 2
-#> [1] 3
-#> [1] "Merging 139 communities"
-#> [1] 1
-#> [1] 2
-#> [1] 3
-#> [1] 4
-#> [1] 5
-#> [1] "Merging 18 communities"
-#> [1] 1
-#> [1] 2
-#> [1] "Merging 14 communities"
-#> [1] 1
-#> [1] "Computing node scores..."
-#> [1] 1
-#> [1] 2
-#> [1] 3
-#> [1] 4
-#> [1] 5
-#> [1] 6
-#> [1] 7
-#> [1] 8
-#> [1] 9
-#> [1] 10
-#> [1] 11
-#> [1] 12
-#> [1] 13
-#> [1] 14
+alpaca<- pandaToAlpaca(treated_net, control_net, NULL, verbose=FALSE) +#> [1] "Detecting communities in control network..." +#> Weights detected. Building condor object with weighted edges. +#> [1] "modularity of projected graph 0.482825466382495" +#> [1] "Q = 0.49524047821125" +#> [1] "Q = 0.49524047821125" +#> [1] "Computing differential modularity matrix..." +#> [1] "Computing differential modules..." +#> [1] "Merging 2644 communities" +#> [1] 1 +#> [1] 2 +#> [1] 3 +#> [1] "Merging 138 communities" +#> [1] 1 +#> [1] 2 +#> [1] 3 +#> [1] 4 +#> [1] 5 +#> [1] 6 +#> [1] "Merging 23 communities" +#> [1] 1 +#> [1] 2 +#> [1] 3 +#> [1] "Merging 13 communities" +#> [1] 1 +#> [1] "Computing node scores..." +#> [1] 1 +#> [1] 2 +#> [1] 3 +#> [1] 4 +#> [1] 5 +#> [1] 6 +#> [1] 7 +#> [1] 8 +#> [1] 9 +#> [1] 10 +#> [1] 11 +#> [1] 12 +#> [1] 13
-
-

-More tutorials

-

Browse with browseVignettes("netZooR")

+
+

More tutorials +

+

Browse with browseVignettes("netZooR")

-
-

-Information

+
+

Information +

-sessionInfo()
-#> R version 4.1.1 (2021-08-10)
-#> Platform: x86_64-apple-darwin17.0 (64-bit)
-#> Running under: macOS Mojave 10.14.6
-#> 
-#> Matrix products: default
-#> BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
-#> LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
-#> 
-#> locale:
-#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
-#> 
-#> attached base packages:
-#> [1] parallel  stats     graphics  grDevices utils     datasets  methods  
-#> [8] base     
-#> 
-#> other attached packages:
-#> [1] viridisLite_0.4.0   netZooR_1.1.16      yarn_1.18.0        
-#> [4] pandaR_1.19.5       Biobase_2.52.0      BiocGenerics_0.38.0
-#> [7] reticulate_1.22     igraph_1.2.6       
-#> 
-#> loaded via a namespace (and not attached):
-#>   [1] utf8_1.2.2                  proto_1.0.0                
-#>   [3] R.utils_2.11.0              RUnit_0.4.32               
-#>   [5] tidyselect_1.1.1            RSQLite_2.2.8              
-#>   [7] AnnotationDbi_1.54.1        grid_4.1.1                 
-#>   [9] BiocParallel_1.26.2         base64url_1.4              
-#>  [11] munsell_0.5.0               codetools_0.2-18           
-#>  [13] ragg_1.1.3                  preprocessCore_1.54.0      
-#>  [15] chron_2.3-56                pbdZMQ_0.3-5               
-#>  [17] colorspace_2.0-2            Category_2.58.0            
-#>  [19] filelock_1.0.2              highr_0.9                  
-#>  [21] dplR_1.7.2                  knitr_1.36                 
-#>  [23] uuid_0.1-4                  stats4_4.1.1               
-#>  [25] MatrixGenerics_1.4.3        repr_1.1.3                 
-#>  [27] GenomeInfoDbData_1.2.6      bit64_4.0.5                
-#>  [29] rhdf5_2.36.0                rprojroot_2.0.2            
-#>  [31] downloader_0.4              vctrs_0.3.8                
-#>  [33] generics_0.1.0              xfun_0.26                  
-#>  [35] BiocFileCache_2.0.0         R6_2.5.1                   
-#>  [37] doParallel_1.0.16           GenomeInfoDb_1.28.4        
-#>  [39] illuminaio_0.34.0           RJSONIO_1.3-1.6            
-#>  [41] locfit_1.5-9.4              bitops_1.0-7               
-#>  [43] rhdf5filters_1.4.0          cachem_1.0.6               
-#>  [45] reshape_0.8.8               DelayedArray_0.18.0        
-#>  [47] assertthat_0.2.1            BiocIO_1.2.0               
-#>  [49] scales_1.1.1                nnet_7.3-16                
-#>  [51] gtable_0.3.0                rlang_0.4.11               
-#>  [53] genefilter_1.74.0           systemfonts_1.0.2          
-#>  [55] splines_4.1.1               rtracklayer_1.52.1         
-#>  [57] GEOquery_2.60.0             hexbin_1.28.2              
-#>  [59] reshape2_1.4.4              yaml_2.2.1                 
-#>  [61] backports_1.2.1             GenomicFeatures_1.44.2     
-#>  [63] RBGL_1.68.0                 tools_4.1.1                
-#>  [65] nor1mix_1.3-0               ggplot2_3.3.5              
-#>  [67] ellipsis_0.3.2              gplots_3.1.1               
-#>  [69] jquerylib_0.1.4             RColorBrewer_1.1-2         
-#>  [71] ggdendro_0.1.22             siggenes_1.66.0            
-#>  [73] STRINGdb_2.4.1              gsubfn_0.7                 
-#>  [75] hash_2.2.6.1                Rcpp_1.0.7                 
-#>  [77] plyr_1.8.6                  base64enc_0.1-3            
-#>  [79] sparseMatrixStats_1.4.2     progress_1.2.2             
-#>  [81] zlibbioc_1.38.0             purrr_0.3.4                
-#>  [83] RCurl_1.98-1.5              prettyunits_1.1.1          
-#>  [85] sqldf_0.4-11                openssl_1.4.5              
-#>  [87] bumphunter_1.34.0           S4Vectors_0.30.2           
-#>  [89] cluster_2.1.2               SummarizedExperiment_1.22.0
-#>  [91] here_1.0.1                  fs_1.5.0                   
-#>  [93] magrittr_2.0.1              data.table_1.14.2          
-#>  [95] matrixStats_0.61.0          hms_1.1.1                  
-#>  [97] evaluate_0.14               xtable_1.8-4               
-#>  [99] XML_3.99-0.8                mclust_5.4.7               
-#> [101] IRanges_2.26.0              compiler_4.1.1             
-#> [103] biomaRt_2.48.3              minfi_1.38.0               
-#> [105] tibble_3.1.5                RCy3_2.12.4                
-#> [107] KernSmooth_2.23-20          crayon_1.4.1               
-#> [109] R.oo_1.24.0                 htmltools_0.5.2            
-#> [111] GOstats_2.58.0              mgcv_1.8-36                
-#> [113] tzdb_0.1.2                  tidyr_1.1.4                
-#> [115] DBI_1.1.1                   dbplyr_2.1.1               
-#> [117] MASS_7.3-54                 rappdirs_0.3.3             
-#> [119] Matrix_1.3-4                readr_2.0.2                
-#> [121] permute_0.9-5               quantro_1.26.0             
-#> [123] uchardet_1.1.0              R.methodsS3_1.8.1          
-#> [125] quadprog_1.5-8              GenomicRanges_1.44.0       
-#> [127] pkgconfig_2.0.3             pkgdown_1.6.1              
-#> [129] GenomicAlignments_1.28.0    signal_0.7-7               
-#> [131] IRdisplay_1.0               xml2_1.3.2                 
-#> [133] foreach_1.5.1               annotate_1.70.0            
-#> [135] penalized_0.9-51            bslib_0.3.1                
-#> [137] rngtools_1.5                multtest_2.48.0            
-#> [139] beanplot_1.2                XVector_0.32.0             
-#> [141] AnnotationForge_1.34.0      doRNG_1.8.2                
-#> [143] scrime_1.3.5                stringr_1.4.0              
-#> [145] digest_0.6.28               vegan_2.5-7                
-#> [147] graph_1.70.0                Biostrings_2.60.2          
-#> [149] rmarkdown_2.11              base64_2.0                 
-#> [151] edgeR_3.34.1                DelayedMatrixStats_1.14.3  
-#> [153] GSEABase_1.54.0             restfulr_0.0.13            
-#> [155] curl_4.3.2                  Rsamtools_2.8.0            
-#> [157] gtools_3.9.2                rjson_0.2.20               
-#> [159] lifecycle_1.0.1             nlme_3.1-152               
-#> [161] jsonlite_1.7.2              Rhdf5lib_1.14.2            
-#> [163] desc_1.4.0                  askpass_1.1                
-#> [165] limma_3.48.3                fansi_0.5.0                
-#> [167] pillar_1.6.3                lattice_0.20-44            
-#> [169] plotrix_3.8-2               KEGGREST_1.32.0            
-#> [171] fastmap_1.1.0               httr_1.4.2                 
-#> [173] survival_3.2-11             GO.db_3.13.0               
-#> [175] glue_1.4.2                  png_0.1-7                  
-#> [177] iterators_1.0.13            bit_4.0.4                  
-#> [179] Rgraphviz_2.36.0            stringi_1.7.5              
-#> [181] sass_0.4.0                  HDF5Array_1.20.0           
-#> [183] blob_1.2.2                  textshaping_0.3.5          
-#> [185] org.Hs.eg.db_3.13.0         caTools_1.18.2             
-#> [187] memoise_2.0.0               IRkernel_1.2               
-#> [189] dplyr_1.0.7
+sessionInfo() +#> R version 4.2.2 (2022-10-31) +#> Platform: x86_64-apple-darwin17.0 (64-bit) +#> Running under: macOS Big Sur ... 10.16 +#> +#> Matrix products: default +#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib +#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib +#> +#> locale: +#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 +#> +#> attached base packages: +#> [1] stats graphics grDevices utils datasets methods base +#> +#> other attached packages: +#> [1] viridisLite_0.4.2 netZooR_1.5.0 matrixcalc_1.0-6 +#> [4] yarn_1.24.0 pandaR_1.19.6 Biobase_2.58.0 +#> [7] BiocGenerics_0.44.0 reticulate_1.32.0 igraph_1.5.1 +#> +#> loaded via a namespace (and not attached): +#> [1] rappdirs_0.3.3 rtracklayer_1.58.0 +#> [3] pbdZMQ_0.3-10 AnnotationForge_1.40.0 +#> [5] ragg_1.2.5 tidyr_1.3.0 +#> [7] bumphunter_1.40.0 minfi_1.44.0 +#> [9] ggplot2_3.4.3 bit64_4.0.5 +#> [11] knitr_1.44 DelayedArray_0.24.0 +#> [13] data.table_1.14.8 KEGGREST_1.38.0 +#> [15] RCurl_1.98-1.12 GEOquery_2.66.0 +#> [17] doParallel_1.0.17 generics_0.1.3 +#> [19] GenomicFeatures_1.50.3 preprocessCore_1.60.1 +#> [21] RSQLite_2.3.1 chron_2.3-61 +#> [23] bit_4.0.5 tzdb_0.4.0 +#> [25] base64url_1.4 xml2_1.3.5 +#> [27] SummarizedExperiment_1.28.0 assertthat_0.2.1 +#> [29] STRINGdb_2.10.0 xfun_0.40 +#> [31] hms_1.1.3 jquerylib_0.1.4 +#> [33] evaluate_0.21 fansi_1.0.4 +#> [35] restfulr_0.0.15 scrime_1.3.5 +#> [37] progress_1.2.2 caTools_1.18.2 +#> [39] dbplyr_2.3.3 Rgraphviz_2.42.0 +#> [41] DBI_1.1.3 reshape_0.8.9 +#> [43] tensorA_0.36.2 stats4_4.2.2 +#> [45] purrr_1.0.2 hash_2.2.6.3 +#> [47] dplyr_1.1.3 backports_1.4.1 +#> [49] permute_0.9-7 annotate_1.76.0 +#> [51] biomaRt_2.54.0 sparseMatrixStats_1.10.0 +#> [53] MatrixGenerics_1.10.0 vctrs_0.6.3 +#> [55] cmdstanr_0.6.1.9000 penalized_0.9-52 +#> [57] here_1.0.1 abind_1.4-5 +#> [59] cachem_1.0.8 withr_2.5.0 +#> [61] checkmate_2.2.0 vegan_2.6-4 +#> [63] GenomicAlignments_1.34.0 prettyunits_1.1.1 +#> [65] mclust_6.0.0 cluster_2.1.4 +#> [67] IRdisplay_1.1 crayon_1.5.2 +#> [69] uchardet_1.1.1 genefilter_1.80.2 +#> [71] edgeR_3.40.1 pkgconfig_2.0.3 +#> [73] GenomeInfoDb_1.34.4 nlme_3.1-163 +#> [75] nnet_7.3-19 rlang_1.1.1 +#> [77] RJSONIO_1.3-1.8 lifecycle_1.0.3 +#> [79] downloader_0.4 filelock_1.0.2 +#> [81] BiocFileCache_2.6.0 GOstats_2.64.0 +#> [83] quantro_1.32.0 distributional_0.3.2 +#> [85] rprojroot_2.0.3 matrixStats_1.0.0 +#> [87] graph_1.76.0 rngtools_1.5.2 +#> [89] base64_2.0.1 Matrix_1.6-1.1 +#> [91] IRkernel_1.3.2 Rhdf5lib_1.20.0 +#> [93] base64enc_0.1-3 processx_3.8.2 +#> [95] png_0.1-8 rjson_0.2.21 +#> [97] bitops_1.0-7 KernSmooth_2.23-22 +#> [99] rhdf5filters_1.10.0 Biostrings_2.66.0 +#> [101] blob_1.2.4 DelayedMatrixStats_1.20.0 +#> [103] doRNG_1.8.6 stringr_1.5.0 +#> [105] nor1mix_1.3-0 readr_2.1.4 +#> [107] S4Vectors_0.36.1 scales_1.2.1 +#> [109] memoise_2.0.1 GSEABase_1.60.0 +#> [111] magrittr_2.0.3 plyr_1.8.8 +#> [113] hexbin_1.28.3 gplots_3.1.3 +#> [115] zlibbioc_1.44.0 compiler_4.2.2 +#> [117] BiocIO_1.8.0 RColorBrewer_1.1-3 +#> [119] illuminaio_0.40.0 plotrix_3.8-2 +#> [121] Rsamtools_2.14.0 cli_3.6.1 +#> [123] XVector_0.38.0 Category_2.64.0 +#> [125] ps_1.7.5 MASS_7.3-60 +#> [127] mgcv_1.9-0 tidyselect_1.2.0 +#> [129] stringi_1.7.12 textshaping_0.3.6 +#> [131] yaml_2.3.7 askpass_1.2.0 +#> [133] locfit_1.5-9.8 grid_4.2.2 +#> [135] sass_0.4.7 tools_4.2.2 +#> [137] parallel_4.2.2 rstudioapi_0.15.0 +#> [139] uuid_1.1-1 foreach_1.5.2 +#> [141] posterior_1.4.1 farver_2.1.1 +#> [143] digest_0.6.33 proto_1.0.0 +#> [145] quadprog_1.5-8 Rcpp_1.0.11 +#> [147] GenomicRanges_1.50.2 siggenes_1.72.0 +#> [149] org.Hs.eg.db_3.16.0 httr_1.4.7 +#> [151] ggdendro_0.1.23 AnnotationDbi_1.60.0 +#> [153] colorspace_2.1-0 XML_3.99-0.14 +#> [155] fs_1.6.3 IRanges_2.32.0 +#> [157] splines_4.2.2 RBGL_1.74.0 +#> [159] pkgdown_2.0.7 multtest_2.54.0 +#> [161] systemfonts_1.0.4 xtable_1.8-4 +#> [163] jsonlite_1.8.7 R6_2.5.1 +#> [165] RUnit_0.4.32 gsubfn_0.7 +#> [167] pillar_1.9.0 htmltools_0.5.6 +#> [169] glue_1.6.2 fastmap_1.1.1 +#> [171] BiocParallel_1.32.4 beanplot_1.3.1 +#> [173] codetools_0.2-19 utf8_1.2.3 +#> [175] lattice_0.21-8 bslib_0.5.1 +#> [177] tibble_3.2.1 sqldf_0.4-11 +#> [179] curl_5.0.2 gtools_3.9.4 +#> [181] GO.db_3.16.0 openssl_2.1.0 +#> [183] survival_3.5-7 limma_3.54.0 +#> [185] rmarkdown_2.25 repr_1.1.6 +#> [187] desc_1.4.2 munsell_0.5.0 +#> [189] rhdf5_2.42.0 GenomeInfoDbData_1.2.9 +#> [191] iterators_1.0.14 RCy3_2.18.0 +#> [193] HDF5Array_1.26.0 reshape2_1.4.4 +#> [195] gtable_0.3.4
-
-

-Note

-

If there is an error like Error in fetch(key) : lazy-load database.rdb' is corrupt when accessing the help pages of functions in this package after being loaded. It’s a limitation of base R and has not been solved yet. Restart R session and re-load this package will help.

+
+

Note +

+

If there is an error like +Error in fetch(key) : lazy-load database.rdb' is corrupt +when accessing the help pages of functions in this package after being +loaded. It’s a +limitation of base R and has not been solved yet. Restart R session +and re-load this package will help.

-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/ApplicationwithTBdataset_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/ApplicationwithTBdataset_files/figure-html/unnamed-chunk-8-1.png index 6eddffd7..f6c3ee76 100644 Binary files a/docs/articles/ApplicationwithTBdataset_files/figure-html/unnamed-chunk-8-1.png and b/docs/articles/ApplicationwithTBdataset_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/docs/articles/CONDOR.html b/docs/articles/CONDOR.html index 9278d21d..bd0dd03f 100644 --- a/docs/articles/CONDOR.html +++ b/docs/articles/CONDOR.html @@ -4,373 +4,360 @@ - + + Using CONDOR for community detection in bipartite graphs • netZooR - - - + + + - - + - -
-
-
-
-

Using CONDOR for community detection in bipartite graphs

-

John Platig

+ + +
+
+

Using CONDOR for community detection in bipartite graphs

+

John +Platig

-

2015-09-28

+

2015-09-28

- Source: vignettes/CONDOR.Rmd - - + Source: vignettes/CONDOR.Rmd +
CONDOR.Rmd
-
-

-Introduction

-

COmplex Network Description Of Regulators (CONDOR) implements methods for clustering bipartite networks and estimating the contribution of each node to its community’s modularity. For an application of this method to identify diesease-associated single nucleotide polymorphisms, see (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005033).

+
+

Introduction +

+

COmplex Network Description Of Regulators (CONDOR) implements methods +for clustering bipartite networks and estimating the contribution of +each node to its community’s modularity. For an application of this +method to identify diesease-associated single nucleotide polymorphisms, +see (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005033).

-
-

-Installation

+
+

Installation +

CONDOR can be installed through netZooR as follows:

-if(!requireNamespace("BiocManager", quietly = TRUE))
-    install.packages("BiocManager")
-BiocManager::install("netZooR")
+if(!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager") +BiocManager::install("netZooR")
-
-

-Implementing the Bipartite Modularity Maximization

-The code in condorModularityMax is an implementation of the method described in Michael Barber’s paper Modularity and community detection in bipartite networks (Phys. Rev. E 76, 066102 (2007)). A few general comments: +
+

Implementing the Bipartite Modularity Maximization +

+The code in condorModularityMax is an implementation of +the method described in Michael Barber’s paper Modularity and +community detection in bipartite networks (Phys. Rev. E 76, +066102 (2007)). A few general comments:
-
-

-Workflow

+
+

Workflow +

-

condor works with an edgelist (elist in the code below) as its input.

+library(netZooR)
+

condor works with an edgelist +(elist in the code below) as its input.

-r = c(1,1,1,2,2,2,3,3,3,4,4);
-b = c(1,2,3,1,2,4,2,3,4,3,4);
-reds <- c("Alice","Sue","Janine","Mary")
-blues <- c("Bob","John","Ed","Hank")
-elist <- data.frame(red=reds[r], blue=blues[b])
-

In elist, notice all nodes of the same type–women and men in this case–appear in the same column together. This is a requirement. createCondorObject will throw an error if a node appears in both columns.

+r = c(1,1,1,2,2,2,3,3,3,4,4); +b = c(1,2,3,1,2,4,2,3,4,3,4); +reds <- c("Alice","Sue","Janine","Mary") +blues <- c("Bob","John","Ed","Hank") +elist <- data.frame(red=reds[r], blue=blues[b])
+

In elist, notice all nodes of the same type–women +and men in this case–appear in the same column together. This is a +requirement. createCondorObject will throw an error if +a node appears in both columns.

-condor.object <- createCondorObject(elist)
-

A condor.object is just a list. You can look at the different items using names

+condor.object <- createCondorObject(elist)
+

A condor.object is just a list. You can look at the different items +using names

-names(condor.object)
-
## [1] "G"          "edges"      "Qcoms"      "modularity" "red.memb"  
-## [6] "blue.memb"  "qscores"
-

condorCluster will cluster the nodes and produce the overall modularity along with two community membership data.frames:

+names(condor.object)
+
## [1] "G"          "edges"      "Qcoms"      "modularity" "red.memb"  
+## [6] "blue.memb"  "qscores"
+

condorCluster will cluster the nodes and produce the +overall modularity along with two community membership +data.frames:

-condor.object <- condorCluster(condor.object)
-
## [1] "modularity of projected graph 0"
-## [1] "Q = 0"
-## [1] "Q = 0.132231404958678"
-## [1] "Q = 0.148760330578512"
-## [1] "Q = 0.148760330578512"
+condor.object <- condorCluster(condor.object)
+
## [1] "modularity of projected graph 0"
+## [1] "Q = 0"
+## [1] "Q = 0.15702479338843"
+## [1] "Q = 0.231404958677686"
+## [1] "Q = 0.231404958677686"
-print(condor.object$red.memb)
-
##   red.names com
-## 1     Alice   1
-## 2    Janine   1
-## 3      Mary   1
-## 4       Sue   2
+print(condor.object$red.memb)
+
##   red.names com
+## 1     Alice   2
+## 2    Janine   1
+## 3      Mary   1
+## 4       Sue   2
-print(condor.object$blue.memb)
-
##   blue.names com
-## 1        Bob   2
-## 2         Ed   1
-## 3       Hank   2
-## 4       John   2
-

Nodes in first community are {Alice, John, Bob, Sue}, nodes in second community are {Ed, Janine, Hank, Mary} based on the modularity maximization. Here’s a picture:

+print(condor.object$blue.memb)
+
##   blue.names com
+## 1        Bob   2
+## 2         Ed   1
+## 3       Hank   1
+## 4       John   2
+

Nodes in first community are {Alice, John, Bob, Sue}, nodes in second +community are {Ed, Janine, Hank, Mary} based on the modularity +maximization. Here’s a picture:

-gtoy = graph.edgelist(as.matrix(elist),directed=FALSE)
-set.graph.attribute(gtoy, "layout", layout.kamada.kawai(gtoy))
-
## IGRAPH d53f3f0 UN-- 8 11 -- 
-## + attr: layout (g/n), name (v/c)
-## + edges from d53f3f0 (vertex names):
-##  [1] Alice--Bob    Alice--John   Alice--Ed     Bob  --Sue    John --Sue   
-##  [6] Sue  --Hank   John --Janine Ed   --Janine Hank --Janine Ed   --Mary  
-## [11] Hank --Mary
+gtoy = graph.edgelist(as.matrix(elist),directed=FALSE) +set.graph.attribute(gtoy, "layout", layout.kamada.kawai(gtoy))
+
## IGRAPH e547bf1 UN-- 8 11 -- 
+## + attr: layout (g/n), name (v/c)
+## + edges from e547bf1 (vertex names):
+##  [1] Alice--Bob    Alice--John   Alice--Ed     Bob  --Sue    John --Sue   
+##  [6] Sue  --Hank   John --Janine Ed   --Janine Hank --Janine Ed   --Mary  
+## [11] Hank --Mary
-V(gtoy)[c(reds,blues)]$color <- c(rep("red",4),rep("blue",4))
+V(gtoy)[c(reds,blues)]$color <- c(rep("red",4),rep("blue",4))
-plot(gtoy,vertex.label.dist=2)
+plot(gtoy,vertex.label.dist=2)

-

To get each node’s modularity contribution (as a fraction of the community’s modularity), run

+

To get each node’s modularity contribution (as a fraction of the +community’s modularity), run

-condor.object <- condorQscore(condor.object)
-

If you have a subset of nodes that you think are more likely to lie at the cores of your communities, you can test this using condorCoreEnrich:

+condor.object <- condorQscore(condor.object)
+

If you have a subset of nodes that you think are more likely to lie +at the cores of your communities, you can test this using +condorCoreEnrich:

-q_women <- condor.object$qscores$red.qscore
-core_stats <- condorCoreEnrich(test_nodes=c("Alice","Mary"),
-                                 q=q_women,perm=TRUE,plot.hist=TRUE)
+q_women <- condor.object$qscores$red.qscore +core_stats <- condorCoreEnrich(test_nodes=c("Alice","Mary"), + q=q_women,perm=TRUE,plot.hist=TRUE)

-

condor also works on weighted bipartite networks. The package comes with a quantitative pollination network data set (Small 1976) taken from the NCEAS interaction webs data base, containing interactions between 13 plants and 34 pollinators.

+

condor also works on weighted bipartite networks. +The package comes with a quantitative pollination network data set +(Small 1976) taken from the NCEAS interaction webs data base, containing +interactions between 13 plants and 34 pollinators.

-data(small1976)
-condor.object <- createCondorObject(small1976)
-condor.object <- condorCluster(condor.object, project=FALSE)
-
## [1] "modularity of projected graph 0.525346928655047"
-## [1] "Q = 0.52666696475026"
-## [1] "Q = 0.52666696475026"
+data(small1976) +condor.object <- createCondorObject(small1976) +condor.object <- condorCluster(condor.object, project=FALSE) +
## [1] "modularity of projected graph 0.525346928655047"
+## [1] "Q = 0.52666696475026"
+## [1] "Q = 0.52666696475026"
-condorPlotHeatmap(condor.object)
+condorPlotHeatmap(condor.object)

-
-

-Session Information

+
+

Session Information +

-
## R version 4.1.1 (2021-08-10)
-## Platform: x86_64-apple-darwin17.0 (64-bit)
-## Running under: macOS Mojave 10.14.6
-## 
-## Matrix products: default
-## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
-## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
-## 
-## locale:
-## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
-## 
-## attached base packages:
-## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
-## [8] base     
-## 
-## other attached packages:
-## [1] netZooR_1.1.16      yarn_1.18.0         pandaR_1.19.5      
-## [4] Biobase_2.52.0      BiocGenerics_0.38.0 reticulate_1.22    
-## [7] igraph_1.2.6       
-## 
-## loaded via a namespace (and not attached):
-##   [1] utf8_1.2.2                  proto_1.0.0                
-##   [3] R.utils_2.11.0              RUnit_0.4.32               
-##   [5] tidyselect_1.1.1            RSQLite_2.2.8              
-##   [7] AnnotationDbi_1.54.1        grid_4.1.1                 
-##   [9] BiocParallel_1.26.2         base64url_1.4              
-##  [11] munsell_0.5.0               codetools_0.2-18           
-##  [13] ragg_1.1.3                  preprocessCore_1.54.0      
-##  [15] chron_2.3-56                pbdZMQ_0.3-5               
-##  [17] colorspace_2.0-2            Category_2.58.0            
-##  [19] filelock_1.0.2              highr_0.9                  
-##  [21] dplR_1.7.2                  knitr_1.36                 
-##  [23] uuid_0.1-4                  stats4_4.1.1               
-##  [25] MatrixGenerics_1.4.3        repr_1.1.3                 
-##  [27] GenomeInfoDbData_1.2.6      bit64_4.0.5                
-##  [29] rhdf5_2.36.0                rprojroot_2.0.2            
-##  [31] downloader_0.4              vctrs_0.3.8                
-##  [33] generics_0.1.0              xfun_0.26                  
-##  [35] BiocFileCache_2.0.0         R6_2.5.1                   
-##  [37] doParallel_1.0.16           GenomeInfoDb_1.28.4        
-##  [39] illuminaio_0.34.0           RJSONIO_1.3-1.6            
-##  [41] locfit_1.5-9.4              bitops_1.0-7               
-##  [43] rhdf5filters_1.4.0          cachem_1.0.6               
-##  [45] reshape_0.8.8               DelayedArray_0.18.0        
-##  [47] assertthat_0.2.1            BiocIO_1.2.0               
-##  [49] scales_1.1.1                nnet_7.3-16                
-##  [51] gtable_0.3.0                rlang_0.4.11               
-##  [53] genefilter_1.74.0           systemfonts_1.0.2          
-##  [55] splines_4.1.1               rtracklayer_1.52.1         
-##  [57] GEOquery_2.60.0             hexbin_1.28.2              
-##  [59] reshape2_1.4.4              yaml_2.2.1                 
-##  [61] backports_1.2.1             GenomicFeatures_1.44.2     
-##  [63] RBGL_1.68.0                 tools_4.1.1                
-##  [65] nor1mix_1.3-0               ggplot2_3.3.5              
-##  [67] ellipsis_0.3.2              gplots_3.1.1               
-##  [69] jquerylib_0.1.4             RColorBrewer_1.1-2         
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-## [187] memoise_2.0.0               IRkernel_1.2               
-## [189] dplyr_1.0.7
+sessionInfo()
+
## R version 4.2.2 (2022-10-31)
+## Platform: x86_64-apple-darwin17.0 (64-bit)
+## Running under: macOS Big Sur ... 10.16
+## 
+## Matrix products: default
+## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
+## 
+## locale:
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+## 
+## attached base packages:
+## [1] stats     graphics  grDevices utils     datasets  methods   base     
+## 
+## other attached packages:
+## [1] netZooR_1.5.0       matrixcalc_1.0-6    yarn_1.24.0        
+## [4] pandaR_1.19.6       Biobase_2.58.0      BiocGenerics_0.44.0
+## [7] reticulate_1.32.0   igraph_1.5.1       
+## 
+## loaded via a namespace (and not attached):
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+## [183] bit_4.0.5                   Rgraphviz_2.42.0           
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+## [191] caTools_1.18.2              memoise_2.0.1              
+## [193] IRkernel_1.3.2              dplyr_1.1.3
- - - - + -
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
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-
-
-
-

Constructing genotype-specific gene regulatory networks with EGRET

-

Deborah Weighill

+ +
+
+

Constructing genotype-specific gene regulatory networks with EGRET

+

Deborah +Weighill

-

2020-11-24

+

2020-11-24

- Source: vignettes/EGRET_toy_example.Rmd - - + Source: vignettes/EGRET_toy_example.Rmd +
EGRET_toy_example.Rmd
-
-

-Introduction

-

EGRET is a method for constructing individual-specific gene regulatory networks (GRNs), taking into account the underlying genotype of the individual in question. EGRET combines multiple lines of evidence (See Figure 1 below) in order to predict the effect of an individual’s mutations on TF-to-gene edges and construct a complete, individual-specific bipartite GRN. TF motifs are used to construct a prior bipartite network of the presence or absence of TFs in the promoter regions of genes. This prior serves as an initial “guess” as to which TFs bind within the promoter regions of, and thus potentially regulate the expression of which genes. This prior is then modified to account for individual-specific genetic information using the individual’s genotype combined with publicly available eQTL data as well as computational predictions of the effects of variants on TF binding using QBiC [1].

-

For a given individual and a given prior edge connecting TF i to gene j, the edge weight is penalized if the individual has a genetic variant meeting 3 conditions, namely, the individual must have (1) an alternate allele at a location within a TF binding motif in the promoter region of a gene, which (2) is an eQTL affecting the expression of the gene adjacent to the promoter and (3) must be predicted by QBiC to affect the binding of the TF corresponding to the motif at that location. Each of these data types is essential to the accurate capturing of variant-derived regulatory disruptions. The altered prior is then integrated with gene expression data and protein-protein interaction information to refine the edge weights using the PANDA message-passing framework [2]. The message-passing algorithm uses the logic that if two genes are co-expressed, they are more likely to be co-regulated and thus are more likely to be regulated by a similar set of TFs; conversely, if two proteins physically interact, they are more likely to bind promoter regions as a complex and thus are more likely to regulate the expression of a similar set of genes. The result is a individual-and-tissue-specific GRN taking into account the genotype information of the individual in question.

+
+

Introduction +

+

EGRET is a method for constructing individual-specific gene +regulatory networks (GRNs), taking into account the underlying genotype +of the individual in question. EGRET combines multiple lines of evidence +(See Figure 1 below) in order to predict the effect of an individual’s +mutations on TF-to-gene edges and construct a complete, +individual-specific bipartite GRN. TF motifs are used to construct a +prior bipartite network of the presence or absence of TFs in the +promoter regions of genes. This prior serves as an initial “guess” as to +which TFs bind within the promoter regions of, and thus potentially +regulate the expression of which genes. This prior is then modified to +account for individual-specific genetic information using the +individual’s genotype combined with publicly available eQTL data as well +as computational predictions of the effects of variants on TF binding +using QBiC [1].

+

For a given individual and a given prior edge connecting TF +i to gene j, the edge weight is +penalized if the individual has a genetic variant meeting 3 conditions, +namely, the individual must have (1) an alternate allele at a location +within a TF binding motif in the promoter region of a gene, which (2) is +an eQTL affecting the expression of the gene adjacent to the promoter +and (3) must be predicted by QBiC to affect the binding of the TF +corresponding to the motif at that location. Each of these data types is +essential to the accurate capturing of variant-derived regulatory +disruptions. The altered prior is then integrated with gene expression +data and protein-protein interaction information to refine the edge +weights using the PANDA message-passing framework [2]. The +message-passing algorithm uses the logic that if two genes are +co-expressed, they are more likely to be co-regulated and thus are more +likely to be regulated by a similar set of TFs; conversely, if two +proteins physically interact, they are more likely to bind promoter +regions as a complex and thus are more likely to regulate the expression +of a similar set of genes. The result is a +individual-and-tissue-specific GRN taking into account the genotype +information of the individual in question.

EGRET has been integrated into the netZooR package.

-
-

Figure 1: EGRET workflow.

+
+Figure 1: EGRET workflow.
+Figure 1: EGRET workflow.
-
-

-Install/load netZooR

-

If you do not have netZooR installed, you can install it from the development branch as follows:

+
+

Install/load netZooR +

+

If you do not have netZooR installed, you can install it from the +development branch as follows:

-#install.packages("devtools")
-#devtools::install_github("netZoo/netZooR@devel")
+#install.packages("devtools") +#devtools::install_github("netZoo/netZooR@devel")

Load the netZooR package:

+library(netZooR)
-
-

-EGRET input data

-
-

-Get the example data sets

+
+

EGRET input data +

+
+

Get the example data sets +

First download the example datasets:

-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_qbic.txt")
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_genotype.vcf")
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_motif_prior.txt")
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_expr.txt")
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_ppi_prior.txt")
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_eQTL.txt")
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_map.txt")
+system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_qbic.txt") +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_genotype.vcf") +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_motif_prior.txt") +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_expr.txt") +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_ppi_prior.txt") +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_eQTL.txt") +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/EGRET/toy_map.txt")

Read in each of the data types.

-qbic <- read.table("toy_qbic.txt", header = FALSE)
-vcf <- read.table("toy_genotype.vcf", header = FALSE, sep = "\t", stringsAsFactors = FALSE, colClasses = c("character", "numeric", "character", "character", "character", "character", "character", "character", "character", "character"))
-motif <- read.table("toy_motif_prior.txt", sep = "\t", header = FALSE)
-expr <- read.table("toy_expr.txt", header = FALSE, sep = "\t", row.names = 1)
-ppi <- read.table("toy_ppi_prior.txt", header = FALSE, sep = "\t")
-qtl <- read.table("toy_eQTL.txt", header = FALSE)
-nameGeneMap <- read.table("toy_map.txt", header = FALSE)
+qbic <- read.table("toy_qbic.txt", header = FALSE) +vcf <- read.table("toy_genotype.vcf", header = FALSE, sep = "\t", stringsAsFactors = FALSE, colClasses = c("character", "numeric", "character", "character", "character", "character", "character", "character", "character", "character")) +motif <- read.table("toy_motif_prior.txt", sep = "\t", header = FALSE) +expr <- read.table("toy_expr.txt", header = FALSE, sep = "\t", row.names = 1) +ppi <- read.table("toy_ppi_prior.txt", header = FALSE, sep = "\t") +qtl <- read.table("toy_eQTL.txt", header = FALSE) +nameGeneMap <- read.table("toy_map.txt", header = FALSE)

Let’s take a look at each of the inputs for EGRET:

-
-

-Motif prior

-

The motif prior is a bipartite network represented as a 3 column data frame. Each row represents an edge in the bipartite graph, with column 1 representing source TFs, column 2 representing target genes and column 3 representing the edge weight. The edge weight represents the presence (edge weight = 1) or absence (edge weight = 0) of the motif corresponding to the TF in column 1 in the promoter region of the gene in column 2. Note that, for ease of differentiating TF nodes from gene nodes, we name TFs with the TF name, and we name genes with their ensembl id.

+
+

Motif prior +

+

The motif prior is a bipartite network represented as a 3 column data +frame. Each row represents an edge in the bipartite graph, with column 1 +representing source TFs, column 2 representing target genes and column 3 +representing the edge weight. The edge weight represents the presence +(edge weight = 1) or absence (edge weight = 0) of the motif +corresponding to the TF in column 1 in the promoter region of the gene +in column 2. Note that, for ease of differentiating TF nodes from gene +nodes, we name TFs with the TF name, and we name genes with their +ensembl id.

-head(motif)
-#>    V1              V2 V3
-#> 1 AHR ENSG00000000419  0
-#> 2 AHR ENSG00000131238  0
-#> 3 AHR ENSG00000184939  0
-#> 4 AHR ENSG00000225077  0
-#> 5 AHR ENSG00000225697  1
-#> 6 AHR ENSG00000254184  0
+head(motif) +#> V1 V2 V3 +#> 1 AHR ENSG00000000419 0 +#> 2 AHR ENSG00000131238 0 +#> 3 AHR ENSG00000184939 0 +#> 4 AHR ENSG00000225077 0 +#> 5 AHR ENSG00000225697 1 +#> 6 AHR ENSG00000254184 0
-
-

-Gene expression

-

The gene expression data represents gene expression measurements (in this case as TPMs from GTEx https://gtexportal.org/home/datasets) across several individuals. These are represented in a data frame with rows corresponding to genes and columns corresponding to samples/individuals. Row names of the data frame should be assigned gene names.

+
+

Gene expression +

+

The gene expression data represents gene expression measurements (in +this case as TPMs from GTEx https://gtexportal.org/home/datasets) across several +individuals. These are represented in a data frame with rows +corresponding to genes and columns corresponding to samples/individuals. +Row names of the data frame should be assigned gene names.

-head(expr)
-#>                       V2       V3      V4      V5     V6       V7       V8
-#> ENSG00000225077   0.2971   0.2846   1.141  0.1843  0.000  0.05798   0.2158
-#> ENSG00000225697  12.3100  16.5400  23.050 15.4700 15.400 14.58000  26.5500
-#> ENSG00000254184   4.6300   2.1590   1.752  3.8840  3.031  1.11900   1.3880
-#> ENSG00000000419 107.8000 100.6000 102.500 91.3400 98.960 93.49000 101.6000
-#>                       V9    V10      V11      V12      V13     V14    V15
-#> ENSG00000225077   0.2398  0.588   0.8401   0.3979   0.5166  0.5822  1.071
-#> ENSG00000225697  15.2900 16.850  11.6900  15.8800  20.1300 20.3000 15.870
-#> ENSG00000254184   1.6610  2.085   1.5290   4.7240   6.4270  5.0950  5.216
-#> ENSG00000000419 113.5000 97.960 119.4000 109.5000 106.5000 96.1200 93.490
-#>                     V16     V17      V18     V19      V20     V21     V22
-#> ENSG00000225077   1.127   1.478   0.4451  0.4858  0.05161  0.3869  0.1505
-#> ENSG00000225697  16.490  18.070  16.6800 20.8900 18.22000 19.2900  8.0150
-#> ENSG00000254184   2.431   6.536   1.4430  1.4420  3.24300  1.6770  2.2760
-#> ENSG00000000419 109.700 108.000 108.2000 99.9000 85.07000 86.7900 90.0500
-#>                    V23     V24     V25      V26     V27     V28     V29
-#> ENSG00000225077  0.592  0.5435  0.4381   0.3502  0.2018  0.3623  0.3682
-#> ENSG00000225697 16.880 14.3600 10.6900  18.0300 13.5000 16.8800 23.9000
-#> ENSG00000254184  4.467  3.7200  1.1110   4.3410  2.3460  3.6920  1.7830
-#> ENSG00000000419 87.740 78.2700 98.1600 120.0000 91.9300 99.4400 72.2100
-#>                      V30    V31     V32      V33     V34      V35      V36
-#> ENSG00000225077  0.02132  0.537  0.3532   0.5425  0.3832   0.2543   0.4901
-#> ENSG00000225697 10.23000  9.273 14.4900  13.0300 15.6300  12.1700  17.2500
-#> ENSG00000254184  2.25800  2.391  4.9570   2.1820  2.1490   1.1500   5.5350
-#> ENSG00000000419 85.47000 96.650 98.1600 109.7000 98.3200 119.1000 112.8000
-#>                     V37     V38      V39     V40      V41      V42    V43
-#> ENSG00000225077  0.3868  0.9251   0.5506  0.7583   0.6189   0.5083  1.092
-#> ENSG00000225697 15.2500 15.9900  12.4800 14.2400  13.0500  11.1400 20.520
-#> ENSG00000254184  3.8270  0.9408   1.8190  4.0410   1.1260   1.4920  2.400
-#> ENSG00000000419 84.8600 79.2100 120.8000 81.0800 117.3000 111.6000 99.170
-#>                     V44    V45     V46      V47      V48      V49     V50
-#> ENSG00000225077  0.2399  0.216  0.1678   0.5535   0.4013   0.8003  0.1167
-#> ENSG00000225697 14.8500 16.140 13.0300  14.6200  13.7400  18.6100 20.3900
-#> ENSG00000254184  1.2660  1.166  2.3430   1.3400   6.4070   1.4990  6.9480
-#> ENSG00000000419 86.6900 87.660 85.9000 112.6000 110.1000 112.4000 97.6500
-#>                     V51     V52      V53      V54     V55     V56     V57
-#> ENSG00000225077  0.2041  0.3714   0.4667  0.09785  0.3234  0.5766  0.2789
-#> ENSG00000225697 29.9200 16.8600  12.4700 20.98000 15.5100 14.1200 17.3700
-#> ENSG00000254184  3.9180  1.6030   4.2750  3.80800  2.1790  1.8850  1.2880
-#> ENSG00000000419 92.9700 83.0500 107.3000 82.69000 92.9400 85.1800 97.9400
-#>                     V58     V59     V60     V61    V62     V63     V64    V65
-#> ENSG00000225077  0.2551  0.2882  0.8827  0.3583  0.515  0.2286  0.2668  0.056
-#> ENSG00000225697 17.1500 10.9700 22.5500 17.3300 16.140 23.4900 19.9500 21.510
-#> ENSG00000254184  1.2810  1.4260  5.3920  1.5840  1.465  2.9400  3.8630  1.676
-#> ENSG00000000419 88.8000 80.1500 97.2900 91.3400 89.260 89.8000 88.9800 87.040
-#>                      V66     V67     V68     V69    V70     V71    V72     V73
-#> ENSG00000225077   0.8874  0.2598  0.1168  0.3319  0.896  0.3382  0.225  0.4426
-#> ENSG00000225697  22.9000 27.5400 16.6000 22.1500 24.570 25.3100 16.820 21.5100
-#> ENSG00000254184   1.6540  1.2210  4.0060  4.9990  1.116  1.2790  1.765  2.3060
-#> ENSG00000000419 103.9000 79.9500 71.9600 87.3200 82.640 69.6100 87.550 85.7400
-#>                    V74     V75     V76     V77     V78      V79      V80
-#> ENSG00000225077  0.361  0.1127  0.1499  0.2601  0.4474  0.05797   0.4357
-#> ENSG00000225697 13.370 16.0600 23.9300 18.7500 14.8600 10.36000  18.0100
-#> ENSG00000254184  1.202  5.4350  1.9430  1.2170  1.1510  2.25100   4.2730
-#> ENSG00000000419 81.560 87.8100 83.2700 81.9000 75.8600 71.91000 100.6000
-#>                      V81    V82     V83     V84      V85     V86    V87     V88
-#> ENSG00000225077  0.06602  0.000  0.4624  0.1525  0.09708  0.9362  0.334  0.1625
-#> ENSG00000225697 17.62000 13.590 13.0300 16.6100 17.41000 18.9900 12.000 13.5000
-#> ENSG00000254184  3.48400  1.479  2.7630  1.7230  1.76500  1.5880  1.322  1.6240
-#> ENSG00000000419 96.09000 82.300 81.2600 87.0900 93.72000 86.4500 75.650 76.3000
-#>                     V89     V90    V91    V92     V93    V94     V95    V96
-#> ENSG00000225077  0.2919  0.3239  0.458  0.421  0.2439  0.441  0.4655  0.000
-#> ENSG00000225697 15.4800 17.1700 14.140 13.530 16.3000 15.690 13.5900 19.420
-#> ENSG00000254184  0.9428  1.1310  1.284  1.339  1.8400  2.323  1.7330  1.616
-#> ENSG00000000419 88.1000 99.8700 80.830 95.850 85.3000 75.690 78.1500 77.850
-#>                     V97     V98     V99    V100     V101     V102    V103
-#> ENSG00000225077  0.3186  0.3548  0.3561  0.2471   0.4384   0.5253  0.3227
-#> ENSG00000225697 17.7500 16.0900  9.3700 16.1800  19.0400  11.5100 18.4500
-#> ENSG00000254184  1.5100  8.4720  2.4930  1.6390   1.2330   1.3960  5.2390
-#> ENSG00000000419 96.0900 83.9500 99.3800 77.5200 119.9000 103.1000 75.2600
-#>                     V104   V105   V106    V107    V108    V109     V110    V111
-#> ENSG00000225077  0.06031  0.221  0.354  0.2368  0.1489   1.057  0.04143   0.413
-#> ENSG00000225697 18.08000 13.780 13.430 18.0600 12.0400  19.120 14.06000  12.360
-#> ENSG00000254184  1.61100  2.542  3.339  1.4330  1.4310   1.513  1.27100   1.151
-#> ENSG00000000419 94.30000 75.860 81.830 98.7200 82.9200 104.100 79.95000 104.100
-#>                    V112   V113    V114    V115    V116     V117    V118
-#> ENSG00000225077  0.3183  0.174  0.1456  0.1846  0.2067   0.1562  0.1587
-#> ENSG00000225697 18.4800 16.350 18.4700 12.5500 16.5900  15.2800 27.2400
-#> ENSG00000254184  3.2670  1.876  1.0920  2.1620  3.7090   1.1590  1.5440
-#> ENSG00000000419 83.3800 94.440 88.2100 98.3100 95.2800 100.6000 95.0900
-#>                     V119    V120   V121     V122   V123   V124    V125    V126
-#> ENSG00000225077   0.9566  0.6961  0.348   0.7355  0.314  0.252  0.5893  0.2411
-#> ENSG00000225697  12.3900 19.0600 12.650  15.1700 19.300 16.770 15.1300 17.6100
-#> ENSG00000254184   2.0510  2.0450  1.768   7.5050  3.593  4.007  6.0080  1.2290
-#> ENSG00000000419 103.8000 80.6500 90.760 126.1000 96.740 85.190 81.0200 97.3700
-#>                    V127    V128    V129     V130    V131
-#> ENSG00000225077  0.1284  0.2762  0.4024   0.6954  0.7493
-#> ENSG00000225697  8.4670 11.0600 16.3800  18.6600 16.6300
-#> ENSG00000254184  1.3850  1.3430  2.0000   3.8730  1.5120
-#> ENSG00000000419 73.6300 86.2600 84.8200 105.0000 99.0400
+head(expr) +#> V2 V3 V4 V5 V6 V7 V8 +#> ENSG00000225077 0.2971 0.2846 1.141 0.1843 0.000 0.05798 0.2158 +#> ENSG00000225697 12.3100 16.5400 23.050 15.4700 15.400 14.58000 26.5500 +#> ENSG00000254184 4.6300 2.1590 1.752 3.8840 3.031 1.11900 1.3880 +#> ENSG00000000419 107.8000 100.6000 102.500 91.3400 98.960 93.49000 101.6000 +#> V9 V10 V11 V12 V13 V14 V15 +#> ENSG00000225077 0.2398 0.588 0.8401 0.3979 0.5166 0.5822 1.071 +#> ENSG00000225697 15.2900 16.850 11.6900 15.8800 20.1300 20.3000 15.870 +#> ENSG00000254184 1.6610 2.085 1.5290 4.7240 6.4270 5.0950 5.216 +#> ENSG00000000419 113.5000 97.960 119.4000 109.5000 106.5000 96.1200 93.490 +#> V16 V17 V18 V19 V20 V21 V22 +#> ENSG00000225077 1.127 1.478 0.4451 0.4858 0.05161 0.3869 0.1505 +#> ENSG00000225697 16.490 18.070 16.6800 20.8900 18.22000 19.2900 8.0150 +#> ENSG00000254184 2.431 6.536 1.4430 1.4420 3.24300 1.6770 2.2760 +#> ENSG00000000419 109.700 108.000 108.2000 99.9000 85.07000 86.7900 90.0500 +#> V23 V24 V25 V26 V27 V28 V29 +#> ENSG00000225077 0.592 0.5435 0.4381 0.3502 0.2018 0.3623 0.3682 +#> ENSG00000225697 16.880 14.3600 10.6900 18.0300 13.5000 16.8800 23.9000 +#> ENSG00000254184 4.467 3.7200 1.1110 4.3410 2.3460 3.6920 1.7830 +#> ENSG00000000419 87.740 78.2700 98.1600 120.0000 91.9300 99.4400 72.2100 +#> V30 V31 V32 V33 V34 V35 V36 +#> ENSG00000225077 0.02132 0.537 0.3532 0.5425 0.3832 0.2543 0.4901 +#> ENSG00000225697 10.23000 9.273 14.4900 13.0300 15.6300 12.1700 17.2500 +#> ENSG00000254184 2.25800 2.391 4.9570 2.1820 2.1490 1.1500 5.5350 +#> ENSG00000000419 85.47000 96.650 98.1600 109.7000 98.3200 119.1000 112.8000 +#> V37 V38 V39 V40 V41 V42 V43 +#> ENSG00000225077 0.3868 0.9251 0.5506 0.7583 0.6189 0.5083 1.092 +#> ENSG00000225697 15.2500 15.9900 12.4800 14.2400 13.0500 11.1400 20.520 +#> ENSG00000254184 3.8270 0.9408 1.8190 4.0410 1.1260 1.4920 2.400 +#> ENSG00000000419 84.8600 79.2100 120.8000 81.0800 117.3000 111.6000 99.170 +#> V44 V45 V46 V47 V48 V49 V50 +#> ENSG00000225077 0.2399 0.216 0.1678 0.5535 0.4013 0.8003 0.1167 +#> ENSG00000225697 14.8500 16.140 13.0300 14.6200 13.7400 18.6100 20.3900 +#> ENSG00000254184 1.2660 1.166 2.3430 1.3400 6.4070 1.4990 6.9480 +#> ENSG00000000419 86.6900 87.660 85.9000 112.6000 110.1000 112.4000 97.6500 +#> V51 V52 V53 V54 V55 V56 V57 +#> ENSG00000225077 0.2041 0.3714 0.4667 0.09785 0.3234 0.5766 0.2789 +#> ENSG00000225697 29.9200 16.8600 12.4700 20.98000 15.5100 14.1200 17.3700 +#> ENSG00000254184 3.9180 1.6030 4.2750 3.80800 2.1790 1.8850 1.2880 +#> ENSG00000000419 92.9700 83.0500 107.3000 82.69000 92.9400 85.1800 97.9400 +#> V58 V59 V60 V61 V62 V63 V64 V65 +#> ENSG00000225077 0.2551 0.2882 0.8827 0.3583 0.515 0.2286 0.2668 0.056 +#> ENSG00000225697 17.1500 10.9700 22.5500 17.3300 16.140 23.4900 19.9500 21.510 +#> ENSG00000254184 1.2810 1.4260 5.3920 1.5840 1.465 2.9400 3.8630 1.676 +#> ENSG00000000419 88.8000 80.1500 97.2900 91.3400 89.260 89.8000 88.9800 87.040 +#> V66 V67 V68 V69 V70 V71 V72 V73 +#> ENSG00000225077 0.8874 0.2598 0.1168 0.3319 0.896 0.3382 0.225 0.4426 +#> ENSG00000225697 22.9000 27.5400 16.6000 22.1500 24.570 25.3100 16.820 21.5100 +#> ENSG00000254184 1.6540 1.2210 4.0060 4.9990 1.116 1.2790 1.765 2.3060 +#> ENSG00000000419 103.9000 79.9500 71.9600 87.3200 82.640 69.6100 87.550 85.7400 +#> V74 V75 V76 V77 V78 V79 V80 +#> ENSG00000225077 0.361 0.1127 0.1499 0.2601 0.4474 0.05797 0.4357 +#> ENSG00000225697 13.370 16.0600 23.9300 18.7500 14.8600 10.36000 18.0100 +#> ENSG00000254184 1.202 5.4350 1.9430 1.2170 1.1510 2.25100 4.2730 +#> ENSG00000000419 81.560 87.8100 83.2700 81.9000 75.8600 71.91000 100.6000 +#> V81 V82 V83 V84 V85 V86 V87 V88 +#> ENSG00000225077 0.06602 0.000 0.4624 0.1525 0.09708 0.9362 0.334 0.1625 +#> ENSG00000225697 17.62000 13.590 13.0300 16.6100 17.41000 18.9900 12.000 13.5000 +#> ENSG00000254184 3.48400 1.479 2.7630 1.7230 1.76500 1.5880 1.322 1.6240 +#> ENSG00000000419 96.09000 82.300 81.2600 87.0900 93.72000 86.4500 75.650 76.3000 +#> V89 V90 V91 V92 V93 V94 V95 V96 +#> ENSG00000225077 0.2919 0.3239 0.458 0.421 0.2439 0.441 0.4655 0.000 +#> ENSG00000225697 15.4800 17.1700 14.140 13.530 16.3000 15.690 13.5900 19.420 +#> ENSG00000254184 0.9428 1.1310 1.284 1.339 1.8400 2.323 1.7330 1.616 +#> ENSG00000000419 88.1000 99.8700 80.830 95.850 85.3000 75.690 78.1500 77.850 +#> V97 V98 V99 V100 V101 V102 V103 +#> ENSG00000225077 0.3186 0.3548 0.3561 0.2471 0.4384 0.5253 0.3227 +#> ENSG00000225697 17.7500 16.0900 9.3700 16.1800 19.0400 11.5100 18.4500 +#> ENSG00000254184 1.5100 8.4720 2.4930 1.6390 1.2330 1.3960 5.2390 +#> ENSG00000000419 96.0900 83.9500 99.3800 77.5200 119.9000 103.1000 75.2600 +#> V104 V105 V106 V107 V108 V109 V110 V111 +#> ENSG00000225077 0.06031 0.221 0.354 0.2368 0.1489 1.057 0.04143 0.413 +#> ENSG00000225697 18.08000 13.780 13.430 18.0600 12.0400 19.120 14.06000 12.360 +#> ENSG00000254184 1.61100 2.542 3.339 1.4330 1.4310 1.513 1.27100 1.151 +#> ENSG00000000419 94.30000 75.860 81.830 98.7200 82.9200 104.100 79.95000 104.100 +#> V112 V113 V114 V115 V116 V117 V118 +#> ENSG00000225077 0.3183 0.174 0.1456 0.1846 0.2067 0.1562 0.1587 +#> ENSG00000225697 18.4800 16.350 18.4700 12.5500 16.5900 15.2800 27.2400 +#> ENSG00000254184 3.2670 1.876 1.0920 2.1620 3.7090 1.1590 1.5440 +#> ENSG00000000419 83.3800 94.440 88.2100 98.3100 95.2800 100.6000 95.0900 +#> V119 V120 V121 V122 V123 V124 V125 V126 +#> ENSG00000225077 0.9566 0.6961 0.348 0.7355 0.314 0.252 0.5893 0.2411 +#> ENSG00000225697 12.3900 19.0600 12.650 15.1700 19.300 16.770 15.1300 17.6100 +#> ENSG00000254184 2.0510 2.0450 1.768 7.5050 3.593 4.007 6.0080 1.2290 +#> ENSG00000000419 103.8000 80.6500 90.760 126.1000 96.740 85.190 81.0200 97.3700 +#> V127 V128 V129 V130 V131 +#> ENSG00000225077 0.1284 0.2762 0.4024 0.6954 0.7493 +#> ENSG00000225697 8.4670 11.0600 16.3800 18.6600 16.6300 +#> ENSG00000254184 1.3850 1.3430 2.0000 3.8730 1.5120 +#> ENSG00000000419 73.6300 86.2600 84.8200 105.0000 99.0400
-
-

-Protein-protein interaction (PPI) data

-

The PPI prior can be obtained from interaction databases such as String (https://string-db.org/). EGRET takes in a PPI network of TFs as a data frame in which each row represents an edge, with columns one and two corresponding to TF nodes and column 3 representing the interaction weight.

+
+

Protein-protein interaction (PPI) data +

+

The PPI prior can be obtained from interaction databases such as +String (https://string-db.org/). EGRET takes in a PPI network of +TFs as a data frame in which each row represents an edge, with columns +one and two corresponding to TF nodes and column 3 representing the +interaction weight.

-head(ppi)
-#>     V1    V2    V3
-#> 1  AHR   AHR 1.000
-#> 2  AHR  ALX3 0.179
-#> 3  AHR GATA2 0.194
-#> 4  AHR GATA4 0.150
-#> 5 ALX3   AHR 0.179
-#> 6 ALX3  ALX3 1.000
+head(ppi) +#> V1 V2 V3 +#> 1 AHR AHR 1.000 +#> 2 AHR ALX3 0.179 +#> 3 AHR GATA2 0.194 +#> 4 AHR GATA4 0.150 +#> 5 ALX3 AHR 0.179 +#> 6 ALX3 ALX3 1.000
-
-

-eQTL data

-

The eQTL data consists of eQTL variants where the eQTL variant lies within a motif within the promoter region of the eGene. These are passed to EGRET as a data frame with the following columns: (1) TF corresponding to the motif in which the eQTL variant resides, (2) eGene adjacent to the promoter, (3) position of the eQTL variant, (4) chromosome on which the eQTL variant and eGene reside, and (5) beta value for the eQTL association. The eQTL data should be from the same cell type/tissue as the gene expression data and can be obtained from databases such as GTEx (https://gtexportal.org/home/datasets).

+
+

eQTL data +

+

The eQTL data consists of eQTL variants where the eQTL variant lies +within a motif within the promoter region of the eGene. These are passed +to EGRET as a data frame with the following columns: (1) TF +corresponding to the motif in which the eQTL variant resides, (2) eGene +adjacent to the promoter, (3) position of the eQTL variant, (4) +chromosome on which the eQTL variant and eGene reside, and (5) beta +value for the eQTL association. The eQTL data should be from the same +cell type/tissue as the gene expression data and can be obtained from +databases such as GTEx (https://gtexportal.org/home/datasets).

-head(qtl)
-#>      V1              V2       V3    V4        V5
-#> 1  ALX3 ENSG00000131238 40563700  chr1 -0.645817
-#> 2 GATA4 ENSG00000184939 68573287 chr16  0.515920
-#> 3  ETS2 ENSG00000225077  6296238  chr1 -0.736242
-#> 4 GATA2 ENSG00000225697 48671834  chr3  0.442918
-#> 5 HOXB8 ENSG00000254184 72209959  chr7  1.365800
+head(qtl) +#> V1 V2 V3 V4 V5 +#> 1 ALX3 ENSG00000131238 40563700 chr1 -0.645817 +#> 2 GATA4 ENSG00000184939 68573287 chr16 0.515920 +#> 3 ETS2 ENSG00000225077 6296238 chr1 -0.736242 +#> 4 GATA2 ENSG00000225697 48671834 chr3 0.442918 +#> 5 HOXB8 ENSG00000254184 72209959 chr7 1.365800
-
-

-Individual genotype

-

The genotype data for the individual in question should be loaded as a VCF file. Columns of the VCF used include column 1 (chromosome), column 2 (variant position), column 4 (reference allele), column 5 (alternate allele) and column 10 (genotype).

+
+

Individual genotype +

+

The genotype data for the individual in question should be loaded as +a VCF file. Columns of the VCF used include column 1 (chromosome), +column 2 (variant position), column 4 (reference allele), column 5 +(alternate allele) and column 10 (genotype).

-head(vcf)
-#>      V1       V2 V3 V4 V5 V6   V7
-#> 1  chr1 40563700  .  T  C  . PASS
-#> 2 chr16 68573287  .  T  C  . PASS
-#> 3  chr1  6296238  .  T  G  . PASS
-#> 4  chr3 48671834  .  T  C  . PASS
-#> 5  chr7 72209959  .  T  G  . PASS
-#>                                                                                      V8
-#> 1 MTD=cgi,bwa_freebayes,bwa_platypus,bwa_gatk,cortex,isaac_strelka;KM=11.95;KFP=0;KFF=0
-#> 2  MTD=cgi,bwa_freebayes,bwa_platypus,bwa_gatk,cortex,isaac_strelka;KM=8.42;KFP=0;KFF=0
-#> 3             MTD=isaac_strelka,bwa_freebayes,bwa_platypus,bwa_gatk;KM=8.96;KFP=0;KFF=0
-#> 4        MTD=cgi,bwa_freebayes,bwa_platypus,isaac_strelka,bwa_gatk;KM=13.15;KFP=0;KFF=0
-#> 5            MTD=isaac_strelka,bwa_freebayes,bwa_platypus,bwa_gatk;KM=14.29;KFP=0;KFF=0
-#>   V9 V10
-#> 1 GT 1|0
-#> 2 GT 1|1
-#> 3 GT 1|1
-#> 4 GT 1|1
-#> 5 GT 0|1
+head(vcf) +#> V1 V2 V3 V4 V5 V6 V7 +#> 1 chr1 40563700 . T C . PASS +#> 2 chr16 68573287 . T C . PASS +#> 3 chr1 6296238 . T G . PASS +#> 4 chr3 48671834 . T C . PASS +#> 5 chr7 72209959 . T G . PASS +#> V8 +#> 1 MTD=cgi,bwa_freebayes,bwa_platypus,bwa_gatk,cortex,isaac_strelka;KM=11.95;KFP=0;KFF=0 +#> 2 MTD=cgi,bwa_freebayes,bwa_platypus,bwa_gatk,cortex,isaac_strelka;KM=8.42;KFP=0;KFF=0 +#> 3 MTD=isaac_strelka,bwa_freebayes,bwa_platypus,bwa_gatk;KM=8.96;KFP=0;KFF=0 +#> 4 MTD=cgi,bwa_freebayes,bwa_platypus,isaac_strelka,bwa_gatk;KM=13.15;KFP=0;KFF=0 +#> 5 MTD=isaac_strelka,bwa_freebayes,bwa_platypus,bwa_gatk;KM=14.29;KFP=0;KFF=0 +#> V9 V10 +#> 1 GT 1|0 +#> 2 GT 1|1 +#> 3 GT 1|1 +#> 4 GT 1|1 +#> 5 GT 0|1
-
-

-QBiC predictions

-

EGRET requires QBiC [1] to be run on the eQTL variants occurring in the individual(s) in question in order to determine which transcription factor’s binding is potentially disrupted due to the variant, at the location of the variant. QBiC makes use of models trained on protein binding microarray (PBM) data to predict the impact of a given variant on TF binding at that location. Some of QBiC’s models are trained on non-human PBMs. We thus require a more stringent filtering (p < 1e-20) of resulting QBiC predictions from non-human models. We also require the predicted effect on binding to be negative (i.e. disruption of binding). QBiC predictions are passed to EGRET in a dataframe with the following columns: (1) variant as chr[num]_position which occurs within a motif in a promoter, (2) TF predicted to be impacted by QBiC, (3) gene adjacent to the promoter, (4) QBiC effect on binding. Note that multiple TFs can be predicted to have disrupted binding at a given variant.

+
+

QBiC predictions +

+

EGRET requires QBiC [1] to be run on the eQTL variants occurring in +the individual(s) in question in order to determine which transcription +factor’s binding is potentially disrupted due to the variant, at the +location of the variant. QBiC makes use of models trained on protein +binding microarray (PBM) data to predict the impact of a given variant +on TF binding at that location. Some of QBiC’s models are trained on +non-human PBMs. We thus require a more stringent filtering (p < +1e-20) of resulting QBiC predictions from non-human models. We also +require the predicted effect on binding to be negative (i.e. disruption +of binding). QBiC predictions are passed to EGRET in a dataframe with +the following columns: (1) variant as chr[num]_position which occurs +within a motif in a promoter, (2) TF predicted to be impacted by QBiC, +(3) gene adjacent to the promoter, (4) QBiC effect on binding. Note that +multiple TFs can be predicted to have disrupted binding at a given +variant.

-head(qbic)
-#>               V1    V2              V3       V4
-#> 1  chr1_40563700  ALX1 ENSG00000131238 -0.72773
-#> 2  chr1_40563700  ALX3 ENSG00000131238 -0.72773
-#> 3  chr1_40563700  ALX4 ENSG00000131238 -0.72773
-#> 4 chr16_68573287 GATA4 ENSG00000184939 -2.56780
-#> 5   chr1_6296238  ETS2 ENSG00000225077 -0.59758
-#> 6  chr3_48671834 GATA2 ENSG00000225697 -2.33451
+head(qbic) +#> V1 V2 V3 V4 +#> 1 chr1_40563700 ALX1 ENSG00000131238 -0.72773 +#> 2 chr1_40563700 ALX3 ENSG00000131238 -0.72773 +#> 3 chr1_40563700 ALX4 ENSG00000131238 -0.72773 +#> 4 chr16_68573287 GATA4 ENSG00000184939 -2.56780 +#> 5 chr1_6296238 ETS2 ENSG00000225077 -0.59758 +#> 6 chr3_48671834 GATA2 ENSG00000225697 -2.33451
-
-

-Run EGRET

-

Set a tag for the EGRET run. The EGRET outputs will be labeled with this tag.

+
+

Run EGRET +

+

Set a tag for the EGRET run. The EGRET outputs will be labeled with +this tag.

-tag <- "my_toy_egret_run"
+tag <- "my_toy_egret_run"

Call the runEgret function to

-runEgret(qtl,vcf,qbic,motif,expr,ppi,nameGeneMap,tag)
-#> [1] "Initializing and validating"
-#> [1] "Verified sufficient samples"
-#> [1] "Normalizing networks..."
-#> [1] "Learning Network..."
-#> [1] "Using tanimoto similarity"
-#> [1] "Initializing and validating"
-#> [1] "Verified sufficient samples"
-#> [1] "Normalizing networks..."
-#> [1] "Learning Network..."
-#> [1] "Using tanimoto similarity"
+runEgret(qtl,vcf,qbic,motif,expr,ppi,nameGeneMap,tag) +#> [1] "Initializing and validating" +#> [1] "Verified sufficient samples" +#> [1] "Normalizing networks..." +#> [1] "Learning Network..." +#> [1] "Using tanimoto similarity" +#> [1] "Initializing and validating" +#> [1] "Verified sufficient samples" +#> [1] "Normalizing networks..." +#> [1] "Learning Network..." +#> [1] "Using tanimoto similarity"
-
-

-EGRET output

-

EGRET produces two output GRNs - a genotype specific “EGRET” network, and a genotype-agnostic baseline network (equivalent to a PANDA network).

+
+

EGRET output +

+

EGRET produces two output GRNs - a genotype specific “EGRET” network, +and a genotype-agnostic baseline network (equivalent to a PANDA +network).

-load("my_toy_egret_run_egret.RData")
-load("my_toy_egret_run_panda.RData")
-
-head(regnetE)
-#>       ENSG00000000419 ENSG00000225077 ENSG00000225697 ENSG00000254184
-#> AHR        -1.7207724      -1.3665641       2.7951309      -2.3822209
-#> ALX3       -1.3200757       1.1089799      -1.4428010       1.0835040
-#> ETS2        0.1649549       2.9397079      -0.7637921      -0.2586327
-#> GATA2      -0.4868319      -0.7719010       3.4413886      -0.9031585
-#> GATA4       1.4572034       0.9393168      -2.8361430       1.0380431
-#> HOXB8       1.3519842      -1.3298778      -1.8452988       2.3230026
-
-head(regnetP)
-#>       ENSG00000000419 ENSG00000225077 ENSG00000225697 ENSG00000254184
-#> AHR        -1.8857284      -1.7359682       3.3731547      -2.7983875
-#> ALX3       -1.3288906       1.4376131      -1.6112997       1.2258982
-#> ETS2        0.8361299       0.8887604       0.7134097       0.5956296
-#> GATA2       0.8361299       0.8887604       0.7134097       0.5956296
-#> GATA4       1.3594558       1.2339543      -2.9404332       1.1808202
-#> HOXB8       1.5258602      -1.4063417      -1.7945491       1.2819613
+load("my_toy_egret_run_egret.RData") +load("my_toy_egret_run_panda.RData") + +head(regnetE) +#> ENSG00000000419 ENSG00000225077 ENSG00000225697 ENSG00000254184 +#> AHR -1.7207724 -1.3665641 2.7951309 -2.3822209 +#> ALX3 -1.3200757 1.1089799 -1.4428010 1.0835040 +#> ETS2 0.1649549 2.9397079 -0.7637921 -0.2586327 +#> GATA2 -0.4868319 -0.7719010 3.4413886 -0.9031585 +#> GATA4 1.4572034 0.9393168 -2.8361430 1.0380431 +#> HOXB8 1.3519842 -1.3298778 -1.8452988 2.3230026 + +head(regnetP) +#> ENSG00000000419 ENSG00000225077 ENSG00000225697 ENSG00000254184 +#> AHR -1.8857284 -1.7359682 3.3731547 -2.7983875 +#> ALX3 -1.3288906 1.4376131 -1.6112997 1.2258982 +#> ETS2 0.8361299 0.8887604 0.7134097 0.5956296 +#> GATA2 0.8361299 0.8887604 0.7134097 0.5956296 +#> GATA4 1.3594558 1.2339543 -2.9404332 1.1808202 +#> HOXB8 1.5258602 -1.4063417 -1.7945491 1.2819613
-
-

-References

+
+

References +

    -
  1. Martin, V., Zhao, J., Afek, A., Mielko, Z. and Gordân, R., 2019. QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants. Nucleic acids research, 47(W1), pp.W127-W135. [https://doi.org/10.1093/nar/gkz363]
    2. -
  2. Glass, K., Huttenhower, C., Quackenbush, J. and Yuan, G.C., 2013. Passing messages between biological networks to refine predicted interactions. PloS one, 8(5), p.e64832. [https://doi.org/10.1371/journal.pone.0064832]
    3. +
  3. Martin, V., Zhao, J., Afek, A., Mielko, Z. and Gordân, R., 2019. +QBiC-Pred: quantitative predictions of transcription factor binding +changes due to sequence variants. Nucleic acids research, 47(W1), +pp.W127-W135. [https://doi.org/10.1093/nar/gkz363]
    4. +
  4. Glass, K., Huttenhower, C., Quackenbush, J. and Yuan, G.C., 2013. +Passing messages between biological networks to refine predicted +interactions. PloS one, 8(5), p.e64832. [https://doi.org/10.1371/journal.pone.0064832]
-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/LionessApplicationinGTExData.html b/docs/articles/LionessApplicationinGTExData.html index 2e4c760b..f4877efa 100644 --- a/docs/articles/LionessApplicationinGTExData.html +++ b/docs/articles/LionessApplicationinGTExData.html @@ -4,447 +4,443 @@ - + + Comparing LIONESS Regulatory Networks using limma • netZooR - - - + + + - - + - -
-
-
-
-

Comparing LIONESS Regulatory Networks using limma

-

Camila Lopes-Ramos

+ + +
+
+

Comparing LIONESS Regulatory Networks using limma

+

Camila +Lopes-Ramos

-

2020-10-19

+

2020-10-19

- Source: vignettes/LionessApplicationinGTExData.Rmd - - + Source: vignettes/LionessApplicationinGTExData.Rmd +
LionessApplicationinGTExData.Rmd
-
-

-Introduction

-

LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) is a method for creating sample-specific networks. When applied to a PANDA regulatory network, the result is a set of gene regulatory networks, one for each sample in the gene expression dataset. More information on LIONESS can be found in the published paper: https://doi.org/10.1016/j.isci.2019.03.021

-

In this vignette, we will compare LIONESS regulatory networks from 207 females and 238 males with colon cancer using RNA-Seq data from TCGA. We will compare the edge weights between females and males using a linear regression model and correcting for the covariates age, race, and disease stage, as available in the limma package. We will also compare the gene’s in-degree (defined as the sum of the gene’s incoming edge weights from all TFs in the network). Finally, we will perform gene set enrichment analysis to find the pathways enriched for genes differentially targeted by sex in colon cancer.

-
-

-Install packages

+
+

Introduction +

+

LIONESS (Linear Interpolation to Obtain Network Estimates for Single +Samples) is a method for creating sample-specific networks. When applied +to a PANDA regulatory network, the result is a set of gene regulatory +networks, one for each sample in the gene expression dataset. More +information on LIONESS can be found in the published paper: https://doi.org/10.1016/j.isci.2019.03.021

+

In this vignette, we will compare LIONESS regulatory networks from +207 females and 238 males with colon cancer using RNA-Seq data from +TCGA. We will compare the edge weights between females and males using a +linear regression model and correcting for the covariates age, race, and +disease stage, as available in the limma package. We will also compare +the gene’s in-degree (defined as the sum of the gene’s incoming edge +weights from all TFs in the network). Finally, we will perform gene set +enrichment analysis to find the pathways enriched for genes +differentially targeted by sex in colon cancer.

+
+

Install packages +

-if (!requireNamespace("BiocManager", quietly = TRUE))   
-    install.packages("BiocManager",repos = "http://cran.us.r-project.org")  
-BiocManager::install("limma")   
-BiocManager::install("fgsea")   
-BiocManager::install("Biobase") 
-# install.packages("ggplot2",repos = "http://cran.us.r-project.org")    
-install.packages("igraph",repos = "http://cran.us.r-project.org")
+if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager",repos = "http://cran.us.r-project.org") +BiocManager::install("limma") +BiocManager::install("fgsea") +BiocManager::install("Biobase") +# install.packages("ggplot2",repos = "http://cran.us.r-project.org") +install.packages("igraph",repos = "http://cran.us.r-project.org")
-
-
-

-Load the data

-

For the purposes of demonstrating the workflow, we will load only a subet of LIONESS networks. Our subset shows the edge weights for 50,000 edges (rows) by 445 samples (columns). Let’s take a look at the networks:

+
+

Load the data +

+

For the purposes of demonstrating the workflow, we will load only a +subet of LIONESS networks. Our subset shows the edge weights for 50,000 +edges (rows) by 445 samples (columns). Let’s take a look at the +networks:

-# download a subset of LIONESS networks from netZooR AWS Bucket to working directory.
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/lioness_coloncancer_subset.txt")
-lioness <- read.delim("./lioness_coloncancer_subset.txt",stringsAsFactors = F, check.names = F)
-head(lioness[,1:5])
-#>       TF  gene TCGA-AY-A71X-01A-12R-A37K-07 TCGA-A6-4105-01A-02R-1774-07
-#> 1  FOXD2 MGST3                       2.3076                       3.2365
-#> 2  MEF2A MGST3                       1.8015                       3.2350
-#> 3    TBP MGST3                       2.2111                       3.1582
-#> 4  TBPL2 GSTA4                       2.2128                       4.5202
-#> 5 POU3F3 MGST2                       4.2174                       5.3111
-#> 6 HMG20B GSTA4                       2.5219                       4.4398
-#>   TCGA-AA-3526-01A-02R-A32Z-07
-#> 1                       3.2611
-#> 2                       3.2992
-#> 3                       3.3390
-#> 4                       4.4191
-#> 5                       5.4973
-#> 6                       4.7402
-# Add row names as "TF_gene" and remove columns 1 and 2 with TF and gene name
-rownames(lioness) <- apply(lioness, 1, function(x){
-  paste(x[1], x[2], sep="_")
-})
-# Remove TF and gene columns
-lioness <- lioness[,-(1:2)]
-head(lioness[,1:5])
-#>              TCGA-AY-A71X-01A-12R-A37K-07 TCGA-A6-4105-01A-02R-1774-07
-#> FOXD2_MGST3                        2.3076                       3.2365
-#> MEF2A_MGST3                        1.8015                       3.2350
-#> TBP_MGST3                          2.2111                       3.1582
-#> TBPL2_GSTA4                        2.2128                       4.5202
-#> POU3F3_MGST2                       4.2174                       5.3111
-#> HMG20B_GSTA4                       2.5219                       4.4398
-#>              TCGA-AA-3526-01A-02R-A32Z-07 TCGA-F4-6463-01A-11R-1723-07
-#> FOXD2_MGST3                        3.2611                       2.7414
-#> MEF2A_MGST3                        3.2992                       2.7100
-#> TBP_MGST3                          3.3390                       2.6938
-#> TBPL2_GSTA4                        4.4191                       4.6907
-#> POU3F3_MGST2                       5.4973                       5.0715
-#> HMG20B_GSTA4                       4.7402                       4.6494
-#>              TCGA-A6-5665-01A-01R-1653-07
-#> FOXD2_MGST3                       -2.5282
-#> MEF2A_MGST3                       -1.7591
-#> TBP_MGST3                         -1.3487
-#> TBPL2_GSTA4                        4.1184
-#> POU3F3_MGST2                       1.5492
-#> HMG20B_GSTA4                       4.1758
-# Load the complete gene in-degree (sum of all edge weights for each gene) and clinical data stored as an expression set
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/inDegree_allEdges_coloncancer.rdata")
-load("./inDegree_allEdges_coloncancer.rdata")
-# Clinical information
-pData(obj1)[1:5,30:35]
-#>                              gender birth_days_to      race
-#> TCGA-AY-A71X-01A-12R-A37K-07 FEMALE        -19904     black
-#> TCGA-A6-4105-01A-02R-1774-07   MALE        -29097 caucasian
-#> TCGA-AA-3526-01A-02R-A32Z-07   MALE        -21123      <NA>
-#> TCGA-F4-6463-01A-11R-1723-07   MALE        -18804 caucasian
-#> TCGA-A6-5665-01A-01R-1653-07 FEMALE        -30915 caucasian
-#>                                           ethnicity history_other_malignancy
-#> TCGA-AY-A71X-01A-12R-A37K-07 NOT HISPANIC OR LATINO                       No
-#> TCGA-A6-4105-01A-02R-1774-07 NOT HISPANIC OR LATINO                      Yes
-#> TCGA-AA-3526-01A-02R-A32Z-07        [Not Available]                       No
-#> TCGA-F4-6463-01A-11R-1723-07 NOT HISPANIC OR LATINO                       No
-#> TCGA-A6-5665-01A-01R-1653-07 NOT HISPANIC OR LATINO                       No
-#>                              history_neoadjuvant_treatment
-#> TCGA-AY-A71X-01A-12R-A37K-07                            No
-#> TCGA-A6-4105-01A-02R-1774-07                            No
-#> TCGA-AA-3526-01A-02R-A32Z-07                            No
-#> TCGA-F4-6463-01A-11R-1723-07                            No
-#> TCGA-A6-5665-01A-01R-1653-07                            No
+# download a subset of LIONESS networks from netZooR AWS Bucket to working directory. +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/lioness_coloncancer_subset.txt") +lioness <- read.delim("./lioness_coloncancer_subset.txt",stringsAsFactors = F, check.names = F) +head(lioness[,1:5]) +#> TF gene TCGA-AY-A71X-01A-12R-A37K-07 TCGA-A6-4105-01A-02R-1774-07 +#> 1 FOXD2 MGST3 2.3076 3.2365 +#> 2 MEF2A MGST3 1.8015 3.2350 +#> 3 TBP MGST3 2.2111 3.1582 +#> 4 TBPL2 GSTA4 2.2128 4.5202 +#> 5 POU3F3 MGST2 4.2174 5.3111 +#> 6 HMG20B GSTA4 2.5219 4.4398 +#> TCGA-AA-3526-01A-02R-A32Z-07 +#> 1 3.2611 +#> 2 3.2992 +#> 3 3.3390 +#> 4 4.4191 +#> 5 5.4973 +#> 6 4.7402 +# Add row names as "TF_gene" and remove columns 1 and 2 with TF and gene name +rownames(lioness) <- apply(lioness, 1, function(x){ + paste(x[1], x[2], sep="_") +}) +# Remove TF and gene columns +lioness <- lioness[,-(1:2)] +head(lioness[,1:5]) +#> TCGA-AY-A71X-01A-12R-A37K-07 TCGA-A6-4105-01A-02R-1774-07 +#> FOXD2_MGST3 2.3076 3.2365 +#> MEF2A_MGST3 1.8015 3.2350 +#> TBP_MGST3 2.2111 3.1582 +#> TBPL2_GSTA4 2.2128 4.5202 +#> POU3F3_MGST2 4.2174 5.3111 +#> HMG20B_GSTA4 2.5219 4.4398 +#> TCGA-AA-3526-01A-02R-A32Z-07 TCGA-F4-6463-01A-11R-1723-07 +#> FOXD2_MGST3 3.2611 2.7414 +#> MEF2A_MGST3 3.2992 2.7100 +#> TBP_MGST3 3.3390 2.6938 +#> TBPL2_GSTA4 4.4191 4.6907 +#> POU3F3_MGST2 5.4973 5.0715 +#> HMG20B_GSTA4 4.7402 4.6494 +#> TCGA-A6-5665-01A-01R-1653-07 +#> FOXD2_MGST3 -2.5282 +#> MEF2A_MGST3 -1.7591 +#> TBP_MGST3 -1.3487 +#> TBPL2_GSTA4 4.1184 +#> POU3F3_MGST2 1.5492 +#> HMG20B_GSTA4 4.1758 +# Load the complete gene in-degree (sum of all edge weights for each gene) and clinical data stored as an expression set +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/inDegree_allEdges_coloncancer.rdata") +load("./inDegree_allEdges_coloncancer.rdata") +# Clinical information +pData(obj1)[1:5,30:35] +#> gender birth_days_to race +#> TCGA-AY-A71X-01A-12R-A37K-07 FEMALE -19904 black +#> TCGA-A6-4105-01A-02R-1774-07 MALE -29097 caucasian +#> TCGA-AA-3526-01A-02R-A32Z-07 MALE -21123 <NA> +#> TCGA-F4-6463-01A-11R-1723-07 MALE -18804 caucasian +#> TCGA-A6-5665-01A-01R-1653-07 FEMALE -30915 caucasian +#> ethnicity history_other_malignancy +#> TCGA-AY-A71X-01A-12R-A37K-07 NOT HISPANIC OR LATINO No +#> TCGA-A6-4105-01A-02R-1774-07 NOT HISPANIC OR LATINO Yes +#> TCGA-AA-3526-01A-02R-A32Z-07 [Not Available] No +#> TCGA-F4-6463-01A-11R-1723-07 NOT HISPANIC OR LATINO No +#> TCGA-A6-5665-01A-01R-1653-07 NOT HISPANIC OR LATINO No +#> history_neoadjuvant_treatment +#> TCGA-AY-A71X-01A-12R-A37K-07 No +#> TCGA-A6-4105-01A-02R-1774-07 No +#> TCGA-AA-3526-01A-02R-A32Z-07 No +#> TCGA-F4-6463-01A-11R-1723-07 No +#> TCGA-A6-5665-01A-01R-1653-07 No
-
-

-Compare the edge weights

-

Compare the edge weights between males and females using linear regression model (limma package) and adjusting for covariates: stage, age, race.

-
-

-Run limma

+
+

Compare the edge weights +

+

Compare the edge weights between males and females using linear +regression model (limma package) and adjusting for covariates: stage, +age, race.

+
+

Run limma +

-# Define the covariates
-gender <- factor(as.character(pData(obj1)$gender),levels=c("MALE","FEMALE"))
-stage <- (as.character(pData(obj1)$uicc_stage))
-stage[which(is.na(stage))] <- "NA"    
-stage <- as.factor(stage)
-race <- as.character(pData(obj1)$race)
-race[which(is.na(race))] <- "NA"
-race <- as.factor(race)
-age <- as.numeric(pData(obj1)$age_at_initial_pathologic_diagnosis)
-age[which(is.na(age))] <- mean(age,na.rm=TRUE)
-design = model.matrix(~ stage + race + age + gender)
-# Run limma
-fitGood = lmFit(as.matrix(lioness),design)
-fitGood = eBayes(fitGood)
-tb = topTable(fitGood,coef="genderFEMALE",number=Inf)
-head(tb)
-#>                     logFC   AveExpr         t      P.Value  adj.P.Val        B
-#> NKX2-5_SLCO3A1 -1.0747338 2.7091520 -4.871484 1.552517e-06 0.04199842 4.751052
-#> SOX12_SLCO3A1  -2.0624528 0.9997061 -4.801388 2.169196e-06 0.04199842 4.456929
-#> TBPL2_SLCO3A1  -2.6081364 0.2806817 -4.708294 3.361432e-06 0.04199842 4.072181
-#> ZNF232_KBTBD2   0.9843244 4.3091270  4.646466 4.478727e-06 0.04199842 3.820378
-#> NFATC3_PORCN   -0.6981933 1.8005115 -4.637017 4.678219e-06 0.04199842 3.782159
-#> FOXO3_PDS5B     0.5946457 4.6099430  4.572347 6.291516e-06 0.04199842 3.522461
+# Define the covariates +gender <- factor(as.character(pData(obj1)$gender),levels=c("MALE","FEMALE")) +stage <- (as.character(pData(obj1)$uicc_stage)) +stage[which(is.na(stage))] <- "NA" +stage <- as.factor(stage) +race <- as.character(pData(obj1)$race) +race[which(is.na(race))] <- "NA" +race <- as.factor(race) +age <- as.numeric(pData(obj1)$age_at_initial_pathologic_diagnosis) +age[which(is.na(age))] <- mean(age,na.rm=TRUE) +design = model.matrix(~ stage + race + age + gender) +# Run limma +fitGood = lmFit(as.matrix(lioness),design) +fitGood = eBayes(fitGood) +tb = topTable(fitGood,coef="genderFEMALE",number=Inf) +head(tb) +#> logFC AveExpr t P.Value adj.P.Val B +#> NKX2-5_SLCO3A1 -1.0747338 2.7091520 -4.871484 1.552517e-06 0.04199842 4.751052 +#> SOX12_SLCO3A1 -2.0624528 0.9997061 -4.801388 2.169196e-06 0.04199842 4.456929 +#> TBPL2_SLCO3A1 -2.6081364 0.2806817 -4.708294 3.361432e-06 0.04199842 4.072181 +#> ZNF232_KBTBD2 0.9843244 4.3091270 4.646466 4.478727e-06 0.04199842 3.820378 +#> NFATC3_PORCN -0.6981933 1.8005115 -4.637017 4.678219e-06 0.04199842 3.782159 +#> FOXO3_PDS5B 0.5946457 4.6099430 4.572347 6.291516e-06 0.04199842 3.522461
-
-

-Visualize the top edges with differential weights by sex

-

We select the top 50 edges with differential edge weights by sex and convert them into an igraph graph.data.frame object for visualization. We color edges red if they have higher coefficients in the female group, and blue if they have higher coefficients in the male group.

+
+

Visualize the top edges with differential weights by sex +

+

We select the top 50 edges with differential edge weights by sex and +convert them into an igraph graph.data.frame object for visualization. +We color edges red if they have higher coefficients in the female group, +and blue if they have higher coefficients in the male group.

-toptable_edges <- t(matrix(unlist(c(strsplit(row.names(tb), "_"))),2))
-z <- cbind(toptable_edges[1:50,], tb$logFC[1:50])
-g <- graph.data.frame(z, directed=FALSE)
-E(g)$weight <- as.numeric(z[,3])
-E(g)$color[E(g)$weight<0] <- "blue"
-E(g)$color[E(g)$weight>0] <- "red"
-E(g)$weight <- 1
-par(mar=c(0,0,0,0))
-plot(g, vertex.label.cex=0.7, vertex.size=10,  vertex.label.font=3, edge.width=5*(abs(as.numeric(z[,3]))))
+toptable_edges <- t(matrix(unlist(c(strsplit(row.names(tb), "_"))),2)) +z <- cbind(toptable_edges[1:50,], tb$logFC[1:50]) +g <- graph.data.frame(z, directed=FALSE) +E(g)$weight <- as.numeric(z[,3]) +E(g)$color[E(g)$weight<0] <- "blue" +E(g)$color[E(g)$weight>0] <- "red" +E(g)$weight <- 1 +par(mar=c(0,0,0,0)) +plot(g, vertex.label.cex=0.7, vertex.size=10, vertex.label.font=3, edge.width=5*(abs(as.numeric(z[,3]))))

-
-

-Compare the gene in-degree

-

Compare the gene in-degree between males and females using linear regression model (limma package) and adjusting for covariates: stage, age, race.

-
-

-Run limma

+
+

Compare the gene in-degree +

+

Compare the gene in-degree between males and females using linear +regression model (limma package) and adjusting for covariates: stage, +age, race.

+
+

Run limma +

-indegree <- assayData(obj1)[["quantile"]]
-head(indegree[,1:3])
-#>        TCGA-AY-A71X-01A-12R-A37K-07 TCGA-A6-4105-01A-02R-1774-07
-#> A1BG                      -300.7761                   -309.57401
-#> A1CF                       129.2204                     37.11690
-#> A2M                        447.1353                    418.45826
-#> A4GALT                    -215.1396                   -155.53291
-#> AAAS                      -342.6264                   -344.35561
-#> AACS                       369.7163                     22.04239
-#>        TCGA-AA-3526-01A-02R-A32Z-07
-#> A1BG                     -321.71607
-#> A1CF                       14.00825
-#> A2M                       397.25374
-#> A4GALT                   -191.21809
-#> AAAS                     -332.18513
-#> AACS                      485.25668
-# Use the same design matrix as before
-fitGood = lmFit(indegree,design)
-fitGood = eBayes(fitGood)
-tb_degree = topTable(fitGood,coef="genderFEMALE",number=Inf)
-head(tb_degree)
-#>             logFC   AveExpr         t      P.Value   adj.P.Val         B
-#> OTUD4   -77.70043  306.3134 -5.223489 2.716224e-07 0.003481384 -4.225218
-#> USP32   -86.32333  468.3938 -4.191101 3.357367e-05 0.089161433 -4.357195
-#> MARCH8 -107.59057  183.7655 -4.172733 3.628637e-05 0.089161433 -4.359326
-#> POLR2G   68.22211 -339.4559  4.089615 5.139259e-05 0.089161433 -4.368867
-#> TBL1X  -171.12318  206.8917 -4.040868 6.285937e-05 0.089161433 -4.374386
-#> GUK1     58.76388 -228.4243  4.030137 6.569152e-05 0.089161433 -4.375593
-# Save gene ranks
-indegree_rank <- setNames(object=tb_degree[,"t"], rownames(tb_degree))
+indegree <- assayData(obj1)[["quantile"]] +head(indegree[,1:3]) +#> TCGA-AY-A71X-01A-12R-A37K-07 TCGA-A6-4105-01A-02R-1774-07 +#> A1BG -300.7761 -309.57401 +#> A1CF 129.2204 37.11690 +#> A2M 447.1353 418.45826 +#> A4GALT -215.1396 -155.53291 +#> AAAS -342.6264 -344.35561 +#> AACS 369.7163 22.04239 +#> TCGA-AA-3526-01A-02R-A32Z-07 +#> A1BG -321.71607 +#> A1CF 14.00825 +#> A2M 397.25374 +#> A4GALT -191.21809 +#> AAAS -332.18513 +#> AACS 485.25668 +# Use the same design matrix as before +fitGood = lmFit(indegree,design) +fitGood = eBayes(fitGood) +tb_degree = topTable(fitGood,coef="genderFEMALE",number=Inf) +head(tb_degree) +#> logFC AveExpr t P.Value adj.P.Val B +#> OTUD4 -77.70043 306.3134 -5.223489 2.716224e-07 0.003481384 -4.225218 +#> USP32 -86.32333 468.3938 -4.191101 3.357367e-05 0.089161433 -4.357195 +#> MARCH8 -107.59057 183.7655 -4.172733 3.628637e-05 0.089161433 -4.359326 +#> POLR2G 68.22211 -339.4559 4.089615 5.139259e-05 0.089161433 -4.368867 +#> TBL1X -171.12318 206.8917 -4.040868 6.285937e-05 0.089161433 -4.374386 +#> GUK1 58.76388 -228.4243 4.030137 6.569152e-05 0.089161433 -4.375593 +# Save gene ranks +indegree_rank <- setNames(object=tb_degree[,"t"], rownames(tb_degree))
-
-

-Gene Set Enrichment Analysis

-

Well will use the fgsea package to perform gene set enrichment analysis. We need to point to a ranked gene list (for example the gene in-degree statistical difference (t value) between males and females), and a list of gene sets (or signatures) in gmt format to test for enrichment. The gene sets can be downloaded from MSigDB: http://software.broadinstitute.org/gsea/msigdb Same gene annotation should be used in the ranked gene list and gene sets.

-
-

-Run fgsea

+
+

Gene Set Enrichment Analysis +

+

Well will use the fgsea package to perform gene set enrichment +analysis. We need to point to a ranked gene list (for example the gene +in-degree statistical difference (t value) between males and females), +and a list of gene sets (or signatures) in gmt format to test for +enrichment. The gene sets can be downloaded from MSigDB: http://software.broadinstitute.org/gsea/msigdb Same gene +annotation should be used in the ranked gene list and gene sets.

+
+

Run fgsea +

-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/c2.cp.kegg.v7.0.symbols.gmt")
-pathways <- gmtPathways("./c2.cp.kegg.v7.0.symbols.gmt")
-fgseaRes <- fgsea(pathways, indegree_rank, minSize=15, maxSize=500, nperm=1000)
-head(fgseaRes)
-#>                                 pathway      pval      padj         ES
-#> 1:      KEGG_GLYCOLYSIS_GLUCONEOGENESIS 0.1187384 0.2145387  0.3240518
-#> 2:         KEGG_CITRATE_CYCLE_TCA_CYCLE 0.5534591 0.6929134 -0.2702159
-#> 3:       KEGG_PENTOSE_PHOSPHATE_PATHWAY 0.2008032 0.3192771  0.3795079
-#> 4: KEGG_FRUCTOSE_AND_MANNOSE_METABOLISM 0.0458891 0.1027657  0.4200039
-#> 5:            KEGG_GALACTOSE_METABOLISM 0.8804781 0.9567555 -0.2250237
-#> 6:           KEGG_FATTY_ACID_METABOLISM 0.1349810 0.2358459  0.3533304
-#>           NES nMoreExtreme size                            leadingEdge
-#> 1:  1.2593265           63   47  ALDOB,TPI1,ALDH3A1,BPGM,PKLR,PDHB,...
-#> 2: -0.9513020          263   29      SDHA,CS,OGDH,PDHA1,ACO2,OGDHL,...
-#> 3:  1.2181659           99   21   ALDOB,DERA,RPE,TALDO1,PGM2,ALDOC,...
-#> 4:  1.4556415           23   30   ALDOB,TSTA3,MTMR6,TPI1,FPGT,PMM2,...
-#> 5: -0.7229038          441   20            HK1,GLA,GANC,PFKL,UGP2,PFKM
-#> 6:  1.2581352           70   33 GCDH,ACAT2,ACOX3,EHHADH,ECHS1,CPT2,...
-# Subset to pathways with FDR < 0.05
-sig <- fgseaRes[fgseaRes$padj < 0.05,]
-# Top 10 pathways enriched in females
-sig$pathway[sig$NES > 0][1:10]
-#>  [1] "KEGG_STEROID_HORMONE_BIOSYNTHESIS"                
-#>  [2] "KEGG_OXIDATIVE_PHOSPHORYLATION"                   
-#>  [3] "KEGG_PYRIMIDINE_METABOLISM"                       
-#>  [4] "KEGG_HISTIDINE_METABOLISM"                        
-#>  [5] "KEGG_GLUTATHIONE_METABOLISM"                      
-#>  [6] "KEGG_TERPENOID_BACKBONE_BIOSYNTHESIS"             
-#>  [7] "KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450"
-#>  [8] "KEGG_DRUG_METABOLISM_CYTOCHROME_P450"             
-#>  [9] "KEGG_RIBOSOME"                                    
-#> [10] "KEGG_DNA_REPLICATION"
-# Top 10 pathways enriched in males
-sig$pathway[sig$NES < 0][1:10]
-#>  [1] "KEGG_INOSITOL_PHOSPHATE_METABOLISM"        
-#>  [2] "KEGG_ABC_TRANSPORTERS"                     
-#>  [3] "KEGG_MAPK_SIGNALING_PATHWAY"               
-#>  [4] "KEGG_ERBB_SIGNALING_PATHWAY"               
-#>  [5] "KEGG_CHEMOKINE_SIGNALING_PATHWAY"          
-#>  [6] "KEGG_PHOSPHATIDYLINOSITOL_SIGNALING_SYSTEM"
-#>  [7] "KEGG_MTOR_SIGNALING_PATHWAY"               
-#>  [8] "KEGG_WNT_SIGNALING_PATHWAY"                
-#>  [9] "KEGG_NOTCH_SIGNALING_PATHWAY"              
-#> [10] "KEGG_AXON_GUIDANCE"
+system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/c2.cp.kegg.v7.0.symbols.gmt") +pathways <- gmtPathways("./c2.cp.kegg.v7.0.symbols.gmt") +fgseaRes <- fgsea(pathways, indegree_rank, minSize=15, maxSize=500, nperm=1000) +head(fgseaRes) +#> pathway pval padj ES +#> 1: KEGG_GLYCOLYSIS_GLUCONEOGENESIS 0.12144213 0.2219460 0.3240518 +#> 2: KEGG_CITRATE_CYCLE_TCA_CYCLE 0.56063618 0.6910167 -0.2702159 +#> 3: KEGG_PENTOSE_PHOSPHATE_PATHWAY 0.19723866 0.3167772 0.3795079 +#> 4: KEGG_FRUCTOSE_AND_MANNOSE_METABOLISM 0.04684318 0.1095304 0.4200039 +#> 5: KEGG_GALACTOSE_METABOLISM 0.89390963 0.9539036 -0.2250237 +#> 6: KEGG_FATTY_ACID_METABOLISM 0.14201183 0.2508876 0.3533304 +#> NES nMoreExtreme size leadingEdge +#> 1: 1.2659396 63 47 ALDOB,TPI1,ALDH3A1,BPGM,PKLR,PDHB,... +#> 2: -0.9412560 281 29 SDHA,CS,OGDH,PDHA1,ACO2,OGDHL,... +#> 3: 1.2098128 99 21 ALDOB,DERA,RPE,TALDO1,PGM2,ALDOC,... +#> 4: 1.4595859 22 30 ALDOB,TSTA3,MTMR6,TPI1,FPGT,PMM2,... +#> 5: -0.7082669 454 20 HK1,GLA,GANC,PFKL,UGP2,PFKM +#> 6: 1.2790493 71 33 GCDH,ACAT2,ACOX3,EHHADH,ECHS1,CPT2,... +# Subset to pathways with FDR < 0.05 +sig <- fgseaRes[fgseaRes$padj < 0.05,] +# Top 10 pathways enriched in females +sig$pathway[sig$NES > 0][1:10] +#> [1] "KEGG_STEROID_HORMONE_BIOSYNTHESIS" +#> [2] "KEGG_OXIDATIVE_PHOSPHORYLATION" +#> [3] "KEGG_PYRIMIDINE_METABOLISM" +#> [4] "KEGG_TERPENOID_BACKBONE_BIOSYNTHESIS" +#> [5] "KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450" +#> [6] "KEGG_DRUG_METABOLISM_CYTOCHROME_P450" +#> [7] "KEGG_RIBOSOME" +#> [8] "KEGG_PROTEASOME" +#> [9] "KEGG_PROTEIN_EXPORT" +#> [10] "KEGG_PEROXISOME" +# Top 10 pathways enriched in males +sig$pathway[sig$NES < 0][1:10] +#> [1] "KEGG_INOSITOL_PHOSPHATE_METABOLISM" +#> [2] "KEGG_ABC_TRANSPORTERS" +#> [3] "KEGG_MAPK_SIGNALING_PATHWAY" +#> [4] "KEGG_ERBB_SIGNALING_PATHWAY" +#> [5] "KEGG_CHEMOKINE_SIGNALING_PATHWAY" +#> [6] "KEGG_PHOSPHATIDYLINOSITOL_SIGNALING_SYSTEM" +#> [7] "KEGG_MTOR_SIGNALING_PATHWAY" +#> [8] "KEGG_WNT_SIGNALING_PATHWAY" +#> [9] "KEGG_NOTCH_SIGNALING_PATHWAY" +#> [10] "KEGG_AXON_GUIDANCE"
-
-

-Bubble plot of differentially targeted pathways by sex

-

Bubble plot of gene sets on y-axis and adjusted p-value (padj) on x-axis. Bubble size indicates the number of genes in each gene set, and bubble color indicates the normalized enrichment score (NES). Blue is for negative NES (enrichment of higher targeted genes in males), and red is for positive NES (enrichment of higher targeted genes in females).

+
+

Bubble plot of differentially targeted pathways by sex +

+

Bubble plot of gene sets on y-axis and adjusted p-value (padj) on +x-axis. Bubble size indicates the number of genes in each gene set, and +bubble color indicates the normalized enrichment score (NES). Blue is +for negative NES (enrichment of higher targeted genes in males), and red +is for positive NES (enrichment of higher targeted genes in +females).

-dat <- data.frame(fgseaRes)
-# Settings
-fdrcut <- 0.05 # FDR cut-off to use as output for significant signatures
-dencol_neg <- "blue" # bubble plot color for negative ES
-dencol_pos <- "red" # bubble plot color for positive ES
-signnamelength <- 4 # set to remove prefix from signature names (2 for "GO", 4 for "KEGG", 8 for "REACTOME")
-asp <- 3 # aspect ratio of bubble plot
-charcut <- 100 # cut signature name in heatmap to this nr of characters
-# Make signature names more readable
-a <- as.character(dat$pathway) # 'a' is a great variable name to substitute row names with something more readable
-for (j in 1:length(a)){
-  a[j] <- substr(a[j], signnamelength+2, nchar(a[j]))
-}
-a <- tolower(a) # convert to lower case (you may want to comment this out, it really depends on what signatures you are looking at, c6 signatures contain gene names, and converting those to lower case may be confusing)
-for (j in 1:length(a)){
-  if(nchar(a[j])>charcut) { a[j] <- paste(substr(a[j], 1, charcut), "...", sep=" ")}
-} # cut signature names that have more characters than charcut, and add "..."
-a <- gsub("_", " ", a)
-dat$NAME <- a
-# Determine what signatures to plot (based on FDR cut)
-dat2 <- dat[dat[,"padj"]<fdrcut,]
-dat2 <- dat2[order(dat2[,"padj"]),] 
-dat2$signature <- factor(dat2$NAME, rev(as.character(dat2$NAME)))
-# Determine what labels to color
-sign_neg <- which(dat2[,"NES"]<0)
-sign_pos <- which(dat2[,"NES"]>0)
-# Color labels
-signcol <- rep(NA, length(dat2$signature))
-signcol[sign_neg] <- dencol_neg # text color of negative signatures
-signcol[sign_pos] <- dencol_pos # text color of positive signatures
-signcol <- rev(signcol) # need to revert vector of colors, because ggplot starts plotting these from below
-# Plot bubble plot
-g<-ggplot(dat2, aes(x=padj,y=signature,size=size))
-g+geom_point(aes(fill=NES), shape=21, colour="white")+
-  theme_bw()+ # white background, needs to be placed before the "signcol" line
-  xlim(0,fdrcut)+
-  scale_size_area(max_size=10,guide="none")+
-  scale_fill_gradient2(low=dencol_neg, high=dencol_pos)+
-  theme(axis.text.y = element_text(colour=signcol))+
-  theme(aspect.ratio=asp, axis.title.y=element_blank()) # test aspect.ratio
+dat <- data.frame(fgseaRes) +# Settings +fdrcut <- 0.05 # FDR cut-off to use as output for significant signatures +dencol_neg <- "blue" # bubble plot color for negative ES +dencol_pos <- "red" # bubble plot color for positive ES +signnamelength <- 4 # set to remove prefix from signature names (2 for "GO", 4 for "KEGG", 8 for "REACTOME") +asp <- 3 # aspect ratio of bubble plot +charcut <- 100 # cut signature name in heatmap to this nr of characters +# Make signature names more readable +a <- as.character(dat$pathway) # 'a' is a great variable name to substitute row names with something more readable +for (j in 1:length(a)){ + a[j] <- substr(a[j], signnamelength+2, nchar(a[j])) +} +a <- tolower(a) # convert to lower case (you may want to comment this out, it really depends on what signatures you are looking at, c6 signatures contain gene names, and converting those to lower case may be confusing) +for (j in 1:length(a)){ + if(nchar(a[j])>charcut) { a[j] <- paste(substr(a[j], 1, charcut), "...", sep=" ")} +} # cut signature names that have more characters than charcut, and add "..." +a <- gsub("_", " ", a) +dat$NAME <- a +# Determine what signatures to plot (based on FDR cut) +dat2 <- dat[dat[,"padj"]<fdrcut,] +dat2 <- dat2[order(dat2[,"padj"]),] +dat2$signature <- factor(dat2$NAME, rev(as.character(dat2$NAME))) +# Determine what labels to color +sign_neg <- which(dat2[,"NES"]<0) +sign_pos <- which(dat2[,"NES"]>0) +# Color labels +signcol <- rep(NA, length(dat2$signature)) +signcol[sign_neg] <- dencol_neg # text color of negative signatures +signcol[sign_pos] <- dencol_pos # text color of positive signatures +signcol <- rev(signcol) # need to revert vector of colors, because ggplot starts plotting these from below +# Plot bubble plot +g<-ggplot(dat2, aes(x=padj,y=signature,size=size)) +g+geom_point(aes(fill=NES), shape=21, colour="white")+ + theme_bw()+ # white background, needs to be placed before the "signcol" line + xlim(0,fdrcut)+ + scale_size_area(max_size=10,guide="none")+ + scale_fill_gradient2(low=dencol_neg, high=dencol_pos)+ + theme(axis.text.y = element_text(colour=signcol))+ + theme(aspect.ratio=asp, axis.title.y=element_blank()) # test aspect.ratio

-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/LionessApplicationinGTExData_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/LionessApplicationinGTExData_files/figure-html/unnamed-chunk-6-1.png index ead6d27f..45f905a5 100644 Binary files a/docs/articles/LionessApplicationinGTExData_files/figure-html/unnamed-chunk-6-1.png and b/docs/articles/LionessApplicationinGTExData_files/figure-html/unnamed-chunk-6-1.png differ diff --git a/docs/articles/LionessApplicationinGTExData_files/figure-html/unnamed-chunk-9-1.png b/docs/articles/LionessApplicationinGTExData_files/figure-html/unnamed-chunk-9-1.png index 7209d23e..1db01080 100644 Binary files a/docs/articles/LionessApplicationinGTExData_files/figure-html/unnamed-chunk-9-1.png and b/docs/articles/LionessApplicationinGTExData_files/figure-html/unnamed-chunk-9-1.png differ diff --git a/docs/articles/MONSTER.html b/docs/articles/MONSTER.html index 190f46d9..0096d8f1 100644 --- a/docs/articles/MONSTER.html +++ b/docs/articles/MONSTER.html @@ -4,160 +4,164 @@ - + + An Introduction to the MONSTER Package • netZooR - - - + + + - - + - -
-
-
-
-

An Introduction to the MONSTER Package

-

Daniel Schlauch

+ + +
+
+

An Introduction to the MONSTER Package

+

Daniel +Schlauch

-

2017-09-22

+

2017-09-22

- Source: vignettes/MONSTER.Rmd - - + Source: vignettes/MONSTER.Rmd +
MONSTER.Rmd
-
-

-Using MONSTER

-

MONSTER (MOdeling Network State Transitions from Expression and Regulatory data) is a tool for identifying the transcription factor drivers of state change.

-

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. This package is an implementation of MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level.

-

MONSTER takes in sequence motif data linking transcription factors (TFs) to genes and gene expression from two conditions. The goal is generate bipartite networks from the gene expression data which quantify evidence of the regulatory roles of each of the TFs to each of the genes. Next, critical TFs are identified by computing a transition matrix, which maps the gene regulatory network in the first state to the gene regulatory network in the second state.

-
-

-Installing netZooR

+
+

Using MONSTER +

+

MONSTER (MOdeling Network State Transitions from Expression and +Regulatory data) is a tool for identifying the transcription factor +drivers of state change.

+

Specific cellular states are often associated with distinct gene +expression patterns. These states are plastic, changing during +development, or in the transition from health to disease. One relatively +simple extension of this concept is to recognize that we can classify +different cell-types by their active gene regulatory networks and that, +consequently, transitions between cellular states can be modeled by +changes in these underlying regulatory networks. This package is an +implementation of MONSTER, MOdeling Network State Transitions from +Expression and Regulatory data, a regression-based method for inferring +transcription factor drivers of cell state conditions at the gene +regulatory network level.

+

MONSTER takes in sequence motif data linking transcription factors +(TFs) to genes and gene expression from two conditions. The goal is +generate bipartite networks from the gene expression data which quantify +evidence of the regulatory roles of each of the TFs to each of the +genes. Next, critical TFs are identified by computing a transition +matrix, which maps the gene regulatory network in the first state to the +gene regulatory network in the second state.

+
+

Installing netZooR +

Install and load netZooR package

-# install.packages("devtools") 
-library(devtools)
-# install netZooR pkg with vignettes, otherwise remove the "build_vignettes = TRUE" argument.
-devtools::install_github("netZoo/netZooR", build_vignettes = TRUE)
+# install.packages("devtools") +library(devtools) +# install netZooR pkg with vignettes, otherwise remove the "build_vignettes = TRUE" argument. +devtools::install_github("netZoo/netZooR", build_vignettes = TRUE)
+library(netZooR)
-
-

-Input files

-

In this demo, we use the included Yeast dataset, containing three separate Yeast datasets, and a sequence motif object. The yeast dataset includes three separate experimental designs each involving microarray assays of 2555 genes of yeast (Saccharomyces cerevisiae). yeast$exp.ko is a set of 106 gene expression samples of following a number of gene knockouts. yeast$exp.cc is a set of 50 gene expression samples taken sets of two across 25 timepoints spanning the cellular cycle. yeast$exp.sr is a set of 173 gene expression samples collected under conditions of stress such as heat shock. Each expression dataset has been normalized and scaled.

-

In this tutorial, we will run monster to identify suspected TF drivers of the change from the early cell cycle to late cell cycle.

-

First, we load the data included in the netZooR package

+
+

Input files +

+

In this demo, we use the included Yeast dataset, containing three +separate Yeast datasets, and a sequence motif object. The +yeast dataset includes three separate experimental designs +each involving microarray assays of 2555 genes of yeast (Saccharomyces +cerevisiae). yeast$exp.ko is a set of 106 gene expression +samples of following a number of gene knockouts. +yeast$exp.cc is a set of 50 gene expression samples taken +sets of two across 25 timepoints spanning the cellular cycle. +yeast$exp.sr is a set of 173 gene expression samples +collected under conditions of stress such as heat shock. Each expression +dataset has been normalized and scaled.

+

In this tutorial, we will run monster to identify +suspected TF drivers of the change from the early cell cycle to late +cell cycle.

+

First, we load the data included in the netZooR +package

-data(yeast)
-

Next, we create our design vector indicating to which group each sample belongs. This vector must contain only 0’s and 1’s (NAs allowed).

-

In this example we are running the analysis on the first 10 timepoints compared to the last 10 timepoints, ignoring the middle 5 for the purposes of simplicity in this tutorial. Each timepoint contains two samples.

+data(yeast)
+

Next, we create our design vector indicating to which group each +sample belongs. This vector must contain only 0’s and 1’s (NAs +allowed).

+

In this example we are running the analysis on the first 10 +timepoints compared to the last 10 timepoints, ignoring the middle 5 for +the purposes of simplicity in this tutorial. Each timepoint contains two +samples.

-design <- c(rep(0,20),rep(NA,10),rep(1,20))
-

The main method in MONSTER is the monster function. This function has three required arguments,

+design <- c(rep(0,20),rep(NA,10),rep(1,20))
+

The main method in MONSTER is the monster function. This +function has three required arguments,

  • A gene expression matrix, yeast$exp.cc
    • @@ -166,1474 +170,1559 @@

  • A design integer vector, design

-

The gene expression argument may be a matrix, a data.frame or an ExpressionSet. In this example, yeast$exp.cc is a data.frame consisting of 2555 rows and 50 columns.

+

The gene expression argument may be a matrix, a +data.frame or an ExpressionSet. In this +example, yeast$exp.cc is a data.frame +consisting of 2555 rows and 50 columns.

The first five rows and columns can be seen

-yeast$exp.cc[1:5,1:5]
-#>                V2        V3       V4        V5       V6
-#> YAL062W -0.410747  0.028904 0.064679  0.123281 0.116928
-#> YAL060W -0.042407 -0.012481 0.194862  0.201449 0.198666
-#> YAL058W -0.039936  0.032378 0.006376 -0.031905 0.036658
-#> YAL012W -0.189982  0.097298 0.159296  0.057393 0.059446
-#> YBR157C  0.219335  0.017866 0.049283 -0.125761 0.003575
-

The motif mapping data.frame tells the MONSTER algorithm which genes contain likely transcription factor binding sites in the vicinity of their promoter. This serves as the regulatory prior and informs the initial network inference method by supplying a partial list of TF targeting.

-

This data.frame contains 3 columns, where each row is a specific edge in the prior network.

+yeast$exp.cc[1:5,1:5] +#> V2 V3 V4 V5 V6 +#> YAL062W -0.410747 0.028904 0.064679 0.123281 0.116928 +#> YAL060W -0.042407 -0.012481 0.194862 0.201449 0.198666 +#> YAL058W -0.039936 0.032378 0.006376 -0.031905 0.036658 +#> YAL012W -0.189982 0.097298 0.159296 0.057393 0.059446 +#> YBR157C 0.219335 0.017866 0.049283 -0.125761 0.003575
+

The motif mapping data.frame tells the MONSTER algorithm which genes +contain likely transcription factor binding sites in the vicinity of +their promoter. This serves as the regulatory prior and informs the +initial network inference method by supplying a partial list of TF +targeting.

+

This data.frame contains 3 columns, where each row is a specific edge +in the prior network.

  • Column 1 specifies the transcription factor for the edge.
  • Column 2 specifies the targeted gene for the edge
    • -
  • Column 3 defines the strength of the edge. By default, in unweighted graphs, this column may be populated entirely with 1’s.
    • +
  • Column 3 defines the strength of the edge. By default, in unweighted +graphs, this column may be populated entirely with 1’s.
-

The set of unique TFs in column 1 and unique genes in column 2 serve to determine the set of TFs and genes that are used in the downstream analysis.

-

The first five rows and columns of the example motif data.frame can be seen

+

The set of unique TFs in column 1 and unique genes in column 2 serve +to determine the set of TFs and genes that are used in the downstream +analysis.

+

The first five rows and columns of the example motif +data.frame can be seen

-yeast$motif[1:5,]
-#>        TF    GENE V3
-#> 1 YLR131C YAL062W  1
-#> 2 YLR131C YBR157C  1
-#> 3 YLR131C YDR277C  1
-#> 4 YLR131C YEL011W  1
-#> 5 YLR131C YFR040W  1
-

MONSTER tests the statistical significance of its results by permuting the samples n times and rerunning the analysis for each permutation. By default, the number of permutations is set to be 100 and can be manually set via the argument nullPerms.

-

Monster is optimized to run on multiple cores, if available. We can specify the maximum number of cores which are available to run the algorithm. If numMaxCores unspecified, MONSTER will check available resources and run on all but four of the available cores.

+yeast$motif[1:5,] +#> TF GENE V3 +#> 1 YLR131C YAL062W 1 +#> 2 YLR131C YBR157C 1 +#> 3 YLR131C YDR277C 1 +#> 4 YLR131C YEL011W 1 +#> 5 YLR131C YFR040W 1
+

MONSTER tests the statistical significance of its results by +permuting the samples n times and rerunning the analysis +for each permutation. By default, the number of permutations is set to +be 100 and can be manually set via the argument +nullPerms.

+

Monster is optimized to run on multiple cores, if available. We can +specify the maximum number of cores which are available to run the +algorithm. If numMaxCores unspecified, MONSTER will check +available resources and run on all but four of the available cores.

-
-

-Running MONSTER

+
+

Running MONSTER +

-yeast$exp.cc[is.na(yeast$exp.cc)] <- mean(as.matrix(yeast$exp.cc),na.rm=TRUE)
-monsterRes <- monster(yeast$exp.cc, design, yeast$motif, nullPerms=100, numMaxCores=1, alphaw=1)
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+yeast$exp.cc[is.na(yeast$exp.cc)] <- mean(as.matrix(yeast$exp.cc),na.rm=TRUE) +monsterRes <- monster(yeast$exp.cc, design, yeast$motif, nullPerms=100, numMaxCores=1, alphaw=1) +#> [1] "101 network transitions to be estimated" +#> [1] "Running iteration 1" +#> [1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 5.698204e-05 secs +#> [1] "More data cleaning" +#> [1] "Main calculation" +#> .....................................................[1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 5.602837e-05 secs +#> [1] "More data cleaning" +#> [1] "Main calculation" +#> .....................................................[1] "Using OLS method" +#> [1] "Finished running iteration 1" +#> [1] "Running iteration 2" +#> [1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 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+#> [1] "Finished running iteration 93" +#> [1] "Running iteration 94" +#> [1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 8.797646e-05 secs +#> [1] "More data cleaning" +#> [1] "Main calculation" +#> .....................................................[1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 7.796288e-05 secs +#> [1] "More data cleaning" +#> [1] "Main calculation" +#> .....................................................[1] "Using OLS method" +#> [1] "Finished running iteration 94" +#> [1] "Running iteration 95" +#> [1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 7.605553e-05 secs +#> [1] "More data cleaning" +#> [1] "Main calculation" +#> .....................................................[1] "Initializing and validating" +#> [1] "Verified 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+#> [1] "Main calculation" +#> .....................................................[1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 8.416176e-05 secs +#> [1] "More data cleaning" +#> [1] "Main calculation" +#> .....................................................[1] "Using OLS method" +#> [1] "Finished running iteration 100" +#> [1] "Running iteration 101" +#> [1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 8.201599e-05 secs +#> [1] "More data cleaning" +#> [1] "Main calculation" +#> .....................................................[1] "Initializing and validating" +#> [1] "Verified adequate samples, calculating correlation matrix" +#> Time difference of 8.893013e-05 secs +#> [1] "More data cleaning" +#> [1] "Main calculation" +#> .....................................................[1] "Using OLS method" +#> [1] "Finished running iteration 101" +#> Time difference of 3.69943 mins

We can print the details of the analysis result

-monsterRes
-#> MONSTER object
-#> 2555 genes
-#> 20 baseline samples
-#> 20 final samples
-#> Transition driven by 53 transcription factors
-#> Run with 100 randomized permutations.
-

In addition to the three required arguments, we have specified that we will compute 100 randomized runs of the analysis to estimate our result’s statistical signficance. MONSTER makes use of parallelization, and we have specified that 4 cores will be used in this analysis to reduce computation time.

-

Our result comes in the form of a monster object, monsterRes, which contains the estimated transition matrix as well as the transition matrices from the 100 null transitions.

+monsterRes +#> MONSTER object +#> 2555 genes +#> 20 baseline samples +#> 20 final samples +#> Transition driven by 53 transcription factors +#> Run with 100 randomized permutations.
+

In addition to the three required arguments, we have specified that +we will compute 100 randomized runs of the analysis to estimate our +result’s statistical signficance. MONSTER makes use of parallelization, +and we have specified that 4 cores will be used in this analysis to +reduce computation time.

+

Our result comes in the form of a monster object, +monsterRes, which contains the estimated transition matrix +as well as the transition matrices from the 100 null transitions.

-
-

-Visualizing MONSTER results

-

Many different plotting options are available in the MONSTER package which make use of additional libraries such as ggplot and igraph. Typically, we are interested in features of the transition matrix, particularly with respect to the distribution of those features under the null.

+
+

Visualizing MONSTER results +

+

Many different plotting options are available in the MONSTER package +which make use of additional libraries such as ggplot and igraph. +Typically, we are interested in features of the transition matrix, +particularly with respect to the distribution of those features under +the null.

-

The main plot function is the dTFI plot, utilizing the ggplot library. Of interest is the degree to which the observed transition matrix differs from those obtained via random premutations of the samples. We quantify this difference via differential TF Involvement, \(dTFI\), defined as the sum of squared off-diagonal elements in each column of the transition matrix, \[\hat{dTFI_{j}}=\frac{\sum_{i=1}^{m}I\left(i\ne j\right)\hat{\tau}_{i,j}^{2}}{\sum_{i=1}^{m}\hat{\tau}_{i,j}^{2}}\]

-

We can view the \(dTFI\) with the generic plot function

+

The main plot function is the dTFI plot, utilizing the +ggplot library. Of interest is the degree to which the +observed transition matrix differs from those obtained via random +premutations of the samples. We quantify this difference via +differential TF Involvement, \(dTFI\), +defined as the sum of squared off-diagonal elements in each column of +the transition matrix, \[\hat{dTFI_{j}}=\frac{\sum_{i=1}^{m}I\left(i\ne +j\right)\hat{\tau}_{i,j}^{2}}{\sum_{i=1}^{m}\hat{\tau}_{i,j}^{2}}\]

+

We can view the \(dTFI\) with the +generic plot function

-monsterPlotMonsterAnalysis(monsterRes)
-#> Warning: Vectorized input to `element_text()` is not officially supported.
-#> Results may be unexpected or may change in future versions of ggplot2.
+monsterPlotMonsterAnalysis(monsterRes) +#> Warning: Vectorized input to `element_text()` is not officially supported. +#> ℹ Results may be unexpected or may change in future versions of ggplot2.

-

In this plot, we have the \(dTFI\) for each transcription factor along the x-axis. The observed values are shown in red and the null values are shown in blue.

-

Due to the complex nature of the structure of gene regulatory networks, it is not uncommon to see transcription factors which exhibit a high degree of transitional change, but which is not statistically significant due to the high variability of that particular TF (e.g. YDR463W). Conversely, some TFs show weak changes, but those changes are large compared to the changes observed in null transitions (e.g. YKL062W). Ideally, we are most interested in TFs which demonstrate large changes in targetting pattern which is found to be strongly significant (e.g. YJL056C).

-

Adding the argument rescale='significance', sorts the x-axis so that the most significant transcription factors are on the left.

+

In this plot, we have the \(dTFI\) +for each transcription factor along the x-axis. The observed values are +shown in red and the null values are shown in blue.

+

Due to the complex nature of the structure of gene regulatory +networks, it is not uncommon to see transcription factors which exhibit +a high degree of transitional change, but which is not statistically +significant due to the high variability of that particular TF +(e.g. YDR463W). Conversely, some TFs show weak changes, but those +changes are large compared to the changes observed in null transitions +(e.g. YKL062W). Ideally, we are most interested in TFs which demonstrate +large changes in targetting pattern which is found to be strongly +significant (e.g. YJL056C).

+

Adding the argument rescale='significance', sorts the +x-axis so that the most significant transcription factors are on the +left.

-monsterPlotMonsterAnalysis(monsterRes, rescale='significance')
-#> Warning: Vectorized input to `element_text()` is not officially supported.
-#> Results may be unexpected or may change in future versions of ggplot2.
+monsterPlotMonsterAnalysis(monsterRes, rescale='significance') +#> Warning: Vectorized input to `element_text()` is not officially supported. +#> ℹ Results may be unexpected or may change in future versions of ggplot2.

-

Our top hit here is YDL056W, which reassuringly is established in the literature as being involved in regulation of cell cycle progression from G1 to S phase [Koch C, et al. (1993)]

+

Our top hit here is YDL056W, which reassuringly is established in the +literature as being involved in regulation of cell cycle progression +from G1 to S phase [Koch C, et al. (1993)]

-

The dTFI plot focuses primarily on individual transcription factors which have systematically changed their targetting patterns between groups. To dive further into the mechanisms, we may be specifically interested in which TFs are acquiring the targetting signatures of which other TFs. We can visualize the transition matrix in a number of ways using MONSTER.

-

First, using the package gplots, we can simply plot the transition matrix. The heatmap.2 function will show the \(m\times m\) transition matrix in the form of a square heatmap with \(m\) being the number of transcription factors. Intuitively, this is the operator, \(\textbf{T}\), on which we transform gene regulatory network \(\textbf{A}\) (The first 10 timepoints in the Yeast Cell Cycle) to network \(\textbf{B}\) (The last 10 timepoints in the Yeast Cell Cycle) via the equation \[\textbf{B}=\textbf{AT} + \textbf{E}\] where \(\textbf{E}\) is the \(p\times m\) error matrix which we are minimizing.

+

The dTFI plot focuses primarily on individual transcription factors +which have systematically changed their targetting patterns between +groups. To dive further into the mechanisms, we may be specifically +interested in which TFs are acquiring the targetting signatures of which +other TFs. We can visualize the transition matrix in a number of ways +using MONSTER.

+

First, using the package gplots, we can simply plot the +transition matrix. The heatmap.2 function will show the +\(m\times m\) transition matrix in the +form of a square heatmap with \(m\) +being the number of transcription factors. Intuitively, this is the +operator, \(\textbf{T}\), on which we +transform gene regulatory network \(\textbf{A}\) (The first 10 timepoints in +the Yeast Cell Cycle) to network \(\textbf{B}\) (The last 10 timepoints in the +Yeast Cell Cycle) via the equation \[\textbf{B}=\textbf{AT} + \textbf{E}\] +where \(\textbf{E}\) is the \(p\times m\) error matrix which we are +minimizing.

-library(gplots)
-heatmap.2(slot(monsterRes, 'tm'), col = "bluered",
-density.info="none",  
-trace="none", 
-dendrogram='none',     
-Rowv=FALSE,
-Colv=FALSE)
+library(gplots) +heatmap.2(slot(monsterRes, 'tm'), col = "bluered", +density.info="none", +trace="none", +dendrogram='none', +Rowv=FALSE, +Colv=FALSE)

-

In examining this heatmap, we are interested in strong deviations from the identity matrix. The diagonal is removed for visualization purposes. We can see that the cell cycle change is strong driven by a handful of transcription factors. Specifically, YBL005W, YLR228C, YLR451W and YML0051W.

-

This transition may also be depicted as a graph, displaying the gain or loss of features between transcription factors. Recall, that a large deviation from zero off of the diagonal indicates that the targetting pattern of one transcription factor is being “transferred” to another transcription factor as we move from the initial state to the final state.

-

MONSTER contains the function transitionNetworkPlot to makes use of the igraph package to display the transition in network states. Since this graph is complete with negative edgeweights allowed, the argument numEdges=20 (default is 100 edges) is used to specify the number of top transitions to display.

+

In examining this heatmap, we are interested in strong deviations +from the identity matrix. The diagonal is removed for visualization +purposes. We can see that the cell cycle change is strong driven by a +handful of transcription factors. Specifically, YBL005W, YLR228C, +YLR451W and YML0051W.

+

This transition may also be depicted as a graph, displaying the gain +or loss of features between transcription factors. Recall, that a large +deviation from zero off of the diagonal indicates that the targetting +pattern of one transcription factor is being “transferred” to another +transcription factor as we move from the initial state to the final +state.

+

MONSTER contains the function +transitionNetworkPlot to makes use of the +igraph package to display the transition in network states. +Since this graph is complete with negative edgeweights allowed, the +argument numEdges=20 (default is 100 edges) is used to +specify the number of top transitions to display.

-monsterTransitionNetworkPlot(monsterRes, numEdges=20)
+monsterTransitionNetworkPlot(monsterRes, numEdges=20)

-

A network visualization of the strongest 20 transitions identified based on the transition matrix above. Arrows indicate a change in edges from a transcription factor in the network of the first 10 timepoints in the Yeast Cell Cycle to resemble those of a transcription factor in the last 10 timepoints in the Yeast Cell Cycle. Edges are sized according to the magnitude of the transition and nodes (TFs) are sized by the dTFI for that TF. The gain of targeting features is indicated by the color blue while the loss of features is indicated by red.

-

Furthermore, we are often interested in correlated targetting pattern sharing. To find clusters of transcription factor transitions, we can plot the set of TFs onto the first two principal components taken from the transition matrix.

+

A network visualization of the strongest 20 transitions identified +based on the transition matrix above. Arrows indicate a change in edges +from a transcription factor in the network of the first 10 timepoints in +the Yeast Cell Cycle to resemble those of a transcription factor in the +last 10 timepoints in the Yeast Cell Cycle. Edges are sized according to +the magnitude of the transition and nodes (TFs) are sized by the dTFI +for that TF. The gain of targeting features is indicated by the color +blue while the loss of features is indicated by red.

+

Furthermore, we are often interested in correlated targetting pattern +sharing. To find clusters of transcription factor transitions, we can +plot the set of TFs onto the first two principal components taken from +the transition matrix.

-    require(ggplot2)
-    tm.pca <- princomp(slot(monsterRes, 'tm'))
-    odsm <- apply(slot(monsterRes, 'tm'),2,function(x){t(x)%*%x})
-    odsm.scaled <- (odsm-mean(odsm))/sd(odsm)+4
-    scores.pca <- as.data.frame(tm.pca$scores)
-    scores.pca <- cbind(scores.pca,'node.names'=rownames(scores.pca))
-    ggplot(data = scores.pca, aes(x = Comp.1, y = Comp.2, label = node.names)) +
-        geom_hline(yintercept = 0, colour = "gray65") +
-        geom_vline(xintercept = 0, colour = "gray65") +
-        geom_text(size = odsm.scaled) +
-        expand_limits(x=c(-.6,.7))+
-        ggtitle("PCA of transitions of Cell Cycle Transcription Factors in Yeast")
+ require(ggplot2) + tm.pca <- princomp(slot(monsterRes, 'tm')) + odsm <- apply(slot(monsterRes, 'tm'),2,function(x){t(x)%*%x}) + odsm.scaled <- (odsm-mean(odsm))/sd(odsm)+4 + scores.pca <- as.data.frame(tm.pca$scores) + scores.pca <- cbind(scores.pca,'node.names'=rownames(scores.pca)) + ggplot(data = scores.pca, aes(x = Comp.1, y = Comp.2, label = node.names)) + + geom_hline(yintercept = 0, colour = "gray65") + + geom_vline(xintercept = 0, colour = "gray65") + + geom_text(size = odsm.scaled) + + expand_limits(x=c(-.6,.7))+ + ggtitle("PCA of transitions of Cell Cycle Transcription Factors in Yeast")

-

The above plot can also be achieved using the included MONSTER function, monster.transitionPCAPlot(monsterRes).

+

The above plot can also be achieved using the included +MONSTER function, +monster.transitionPCAPlot(monsterRes).

-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

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+

+

Site built with pkgdown 2.0.7.

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-
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-

SAMBAR: Subtyping Agglomerated Mutations By Annotation Relations

-

Marieke L. Kuijjer

+ + +
+
+

SAMBAR: Subtyping Agglomerated Mutations By Annotation Relations

+

Marieke L. +Kuijjer

-

2018-06-05

+

2018-06-05

- Source: vignettes/SAMBAR.Rmd - - + Source: vignettes/SAMBAR.Rmd +
SAMBAR.Rmd
-

SAMBAR, or Subtyping Agglomerated Mutations By Annotation Relations, is a method to identify subtypes based on somatic mutation data. SAMBAR was used to identify mutational subtypes in 23 cancer types from The Cancer Genome Atlas (Kuijjer ML, Paulson JN, Salzman P, Ding W, Quackenbush J, British Journal of Cancer (May 16, 2018), doi: 10.1038/s41416-018-0109-7, https://www.nature.com/articles/s41416-018-0109-7, BioRxiv, doi: https://doi.org/10.1101/228031).

-

SAMBAR’s input is a matrix that includes the number of non-synonymous mutations in a sample \(i\) and gene \(j\) . SAMBAR first subsets these data to a set of 2,219 cancer-associated genes (optional) from the Catalogue Of Somatic Mutations In Cancer (COSMIC) and Östlund et al. (Network-based identification of novel cancer genes, 2010, Mol Cell Prot), or from a user-defined list. It then divides the number of non-synonymous mutations by the gene’s length \(L_j\), defined as the number of non-overlapping exonic base pairs of a gene. For each sample, SAMBAR then calculates the overall cancer-associated mutation rate by summing mutation scores in all cancer-associated genes \(j'\) . It removes samples for which the mutation rate is zero and divides the mutation scores the remaining samples by the sample’s mutation rate, resulting in a matrix of mutation rate-adjusted scores \(G\):

+

SAMBAR, or Subtyping Agglomerated Mutations By Annotation Relations, +is a method to identify subtypes based on somatic mutation data. SAMBAR +was used to identify mutational subtypes in 23 cancer types from The +Cancer Genome Atlas (Kuijjer ML, Paulson JN, Salzman P, Ding W, +Quackenbush J, British Journal of Cancer (May 16, 2018), doi: +10.1038/s41416-018-0109-7, https://www.nature.com/articles/s41416-018-0109-7, +BioRxiv, doi: https://doi.org/10.1101/228031).

+

SAMBAR’s input is a matrix that includes the number of non-synonymous +mutations in a sample \(i\) and gene +\(j\) . SAMBAR first subsets these data +to a set of 2,219 cancer-associated genes (optional) from the Catalogue +Of Somatic Mutations In Cancer (COSMIC) and Östlund et +al. (Network-based identification of novel cancer genes, 2010, Mol Cell +Prot), or from a user-defined list. It then divides the number of +non-synonymous mutations by the gene’s length \(L_j\), defined as the number of +non-overlapping exonic base pairs of a gene. For each sample, SAMBAR +then calculates the overall cancer-associated mutation rate by summing +mutation scores in all cancer-associated genes \(j'\) . It removes samples for which the +mutation rate is zero and divides the mutation scores the remaining +samples by the sample’s mutation rate, resulting in a matrix of mutation +rate-adjusted scores \(G\):

\(G_{ij}=\frac{N_{ij}/L_{j}}{\displaystyle\sum_{j'}({N_{ij'}/L_{j'}})}\)

-

The next step in SAMBAR is de-sparsification of these gene mutation scores (agglomerated mutations) into pathway mutation (annotation relation) scores. SAMBAR converts a (user-defined) gene signature (.gmt format) into a binary matrix \(M\) , with information of whether a gene \(j\) belongs to a pathway \(q\) . It then calculates pathway mutation scores \(P\) by correcting the sum of mutation scores of all genes in a pathway for the number of pathways \(q'\) a gene belongs to, and for the number of cancer-associated genes present in that pathway:

-

\(P_{iq}=\frac{\displaystyle\sum_{j \in q} G_{ij}/{\displaystyle\sum_{q'} M_{jq'}}}{\displaystyle\sum_{j} M_{jq}}\)

-

Finally, SAMBAR uses binomial distance to cluster the pathway mutation scores. The cluster dendrogram is then divided into \(k\) groups (or a range of \(k\) groups), and the cluster assignments are returned in a list.

-
-

-Example: Subtyping Uterine Corpus Endometrial Carcinoma

-

As an example, we’ve added mutation data of Uterine Corpus Endometrial Carcinoma (UCEC) primary tumor samples, obtained from The Cancer Genome Atlas, to this package in data(mut.ucec). We’ve also added gene lengths (the number of non-overlapping exonic base pairs) for 23,459 genes (hg19) in data(exon.size). We’ve added a list of cancer-associated genes in data(gene). Finally, the package includes a .gmt file that contains Hallmarks pathways from MSigDb.

-

To run SAMBAR with default settings on these data: subtypes <- sambar()

-

Or:
Download the signatureset from our public AWS S3 bucket by usingsystem("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/h.all.v6.1.symbols.gmt") and call SAMBAR by using the path where the signatureset is stored, subtypes <- sambar(mutdata=mut.ucec, esize=exon.size, signatureset="./h.all.v6.1.symbols.gmt"), cangenes=genes, kmin=2, kmax=4)

-

This will run SAMBAR on the UCEC mutation data, using desparsification on cancer-associated genes based on the MSigDb “Hallmark” gene sets. It will return a list of samples belonging to \(k=2-4\) subtypes.

-

Instead of using the default signatureset, you can download any .gmt file from MSigDb and add the path to this file in signatureset=PATH_TO_FILE.

+

The next step in SAMBAR is de-sparsification of these gene mutation +scores (agglomerated mutations) into pathway mutation (annotation +relation) scores. SAMBAR converts a (user-defined) gene signature (.gmt +format) into a binary matrix \(M\) , +with information of whether a gene \(j\) belongs to a pathway \(q\) . It then calculates pathway mutation +scores \(P\) by correcting the sum of +mutation scores of all genes in a pathway for the number of pathways +\(q'\) a gene belongs to, and for +the number of cancer-associated genes present in that pathway:

+

\(P_{iq}=\frac{\displaystyle\sum_{j \in q} +G_{ij}/{\displaystyle\sum_{q'} M_{jq'}}}{\displaystyle\sum_{j} +M_{jq}}\)

+

Finally, SAMBAR uses binomial distance to cluster the pathway +mutation scores. The cluster dendrogram is then divided into \(k\) groups (or a range of \(k\) groups), and the cluster assignments +are returned in a list.

+
+

Example: Subtyping Uterine Corpus Endometrial Carcinoma +

+

As an example, we’ve added mutation data of Uterine Corpus +Endometrial Carcinoma (UCEC) primary tumor samples, obtained from The +Cancer Genome Atlas, to this package in data(mut.ucec). +We’ve also added gene lengths (the number of non-overlapping exonic base +pairs) for 23,459 genes (hg19) in data(exon.size). We’ve +added a list of cancer-associated genes in data(gene). +Finally, the package includes a .gmt file that contains Hallmarks +pathways from MSigDb.

+

To run SAMBAR with default settings on these data: +subtypes <- sambar()

+

Or:
Download the signatureset from our public AWS S3 bucket by +usingsystem("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/h.all.v6.1.symbols.gmt") +and call SAMBAR by using the path where the signatureset is stored, +subtypes <- sambar(mutdata=mut.ucec, esize=exon.size, signatureset="./h.all.v6.1.symbols.gmt"), cangenes=genes, kmin=2, kmax=4)

+

This will run SAMBAR on the UCEC mutation data, using +desparsification on cancer-associated genes based on the MSigDb +“Hallmark” gene sets. It will return a list of samples belonging to +\(k=2-4\) subtypes.

+

Instead of using the default signatureset, you can download any .gmt +file from MSigDb and add the path to this file in +signatureset=PATH_TO_FILE.

-
-

-Notes

-

Please note that, in order to be able to cluster the data using the binomial distance, SAMBAR removes samples that have mutation scores of 0 across all pathways, and pathways that have mutation scores of 0 across all samples. Using a small number of genes of interest (cangenes) or a pathways that include small numbers of genes could result in errors. We have not yet added any user-friendly error checks to the package but plan to do this in the future.

+
+

Notes +

+

Please note that, in order to be able to cluster the data using the +binomial distance, SAMBAR removes samples that have mutation scores of 0 +across all pathways, and pathways that have mutation scores of 0 across +all samples. Using a small number of genes of interest +(cangenes) or a pathways that include small numbers of +genes could result in errors. We have not yet added any user-friendly +error checks to the package but plan to do this in the future.

-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/TutorialOTTER.html b/docs/articles/TutorialOTTER.html index f98373fa..1777cb0a 100644 --- a/docs/articles/TutorialOTTER.html +++ b/docs/articles/TutorialOTTER.html @@ -4,477 +4,534 @@ - + + Inferring Gene Regulatory Networks from GTEx Gene Expression Data in R with OTTER • netZooR - - - + + + - - + - -
-
-
-
-

Inferring Gene Regulatory Networks from GTEx Gene Expression Data in R with OTTER

-

Rebekka Burkholz

+ + +
+
+

Inferring Gene Regulatory Networks from GTEx Gene Expression Data in R with OTTER

+

Rebekka +Burkholz

-

2020-9-22

+

2020-9-22

- Source: vignettes/TutorialOTTER.Rmd - - + Source: vignettes/TutorialOTTER.Rmd +
TutorialOTTER.Rmd
-
-

-Introduction

-

In this tutorial, we will learn how to infer OTTER gene regulatory networks from gene expression data with netZooR. It is an adaptation of the PANDA tutorial written by Camila Lopes-Ramos. We will focus on two examples: (1) the LCL cell line and (2) whole blood samples from the GTEx gene expression data. OTTER has been developed from a machine learning perspective in close analogy to PANDA. For this reason, we will follow the same steps as in the respective tutorial for PANDA.

-

First, we will build one regulatory network for LCL cell line samples and one for whole blood samples. Next, we will compare the two networks by a pathway enrichment analysis for differentially targeted genes.

-

Cell lines are an essential tool in biomedical research and are often used as surrogates for tissues. LCLs (obtained from the transformation of B cells present in whole blood) are among the most widely used continuous cell lines with the ability to proliferate indefinitely. By comparing the regulatory networks of LCL cell lines with its tissue of origin (whole blood), we find that LCLs exhibit large changes in their patterns of transcription factor regulation, specifically a loss of repressive transcription factor targeting of cell cycle genes.

-
-

-Install packages

+
+

Introduction +

+

In this tutorial, we will learn how to infer OTTER gene regulatory +networks from gene expression data with netZooR. It is an adaptation of +the PANDA tutorial written by Camila Lopes-Ramos. We will focus on two +examples: (1) the LCL cell line and (2) whole blood samples from the +GTEx gene expression data. OTTER has been developed from a machine +learning perspective in close analogy to PANDA. For this reason, we will +follow the same steps as in the respective tutorial for PANDA.

+

First, we will build one regulatory network for LCL cell line samples +and one for whole blood samples. Next, we will compare the two networks +by a pathway enrichment analysis for differentially targeted genes.

+

Cell lines are an essential tool in biomedical research and are often +used as surrogates for tissues. LCLs (obtained from the transformation +of B cells present in whole blood) are among the most widely used +continuous cell lines with the ability to proliferate indefinitely. By +comparing the regulatory networks of LCL cell lines with its tissue of +origin (whole blood), we find that LCLs exhibit large changes in their +patterns of transcription factor regulation, specifically a loss of +repressive transcription factor targeting of cell cycle genes.

+
+

Install packages +

-if (!requireNamespace("BiocManager", quietly = TRUE))
-    install.packages("BiocManager",repos = "http://cran.us.r-project.org")
-BiocManager::install("fgsea")
-install.packages("reshape2",repos = "http://cran.us.r-project.org")
-install.packages("ggplot2",repos = "http://cran.us.r-project.org")
+if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager",repos = "http://cran.us.r-project.org") +BiocManager::install("fgsea") +install.packages("reshape2",repos = "http://cran.us.r-project.org") +install.packages("ggplot2",repos = "http://cran.us.r-project.org")
-install.packages("devtools")
-library(devtools)
-devtools::install_github("netZoo/netZooR", build_vignettes = TRUE)
+install.packages("devtools") +library(devtools) +devtools::install_github("netZoo/netZooR", build_vignettes = TRUE)
-
-
-

-OTTER

-
-

-Background of OTTER

-

OTTER (Optimize to Estimate Regulation) is a general method to infer a bipartite network \(W\) from noisy observations of its projections \(WW^T\) and \(W^TW\). It is explained in detail in the accompanying publication https://www.biorxiv.org/content/10.1101/2020.06.23.167999v2. In this tutorial, we are particularly interested in constructing a gene regulatory network \(W\) between transcription factors (TFs) and genes. Higher link weights are associated with a higher probability of TFs binding to the promoter region of a gene. Inference of a bipartite gene regulatory network between transcription factors (TFs) and genes.

-

OTTER requires the following inputs: (1) a correlation matrix \(C\), which is based on gene expression data, (2) protein-protein interactions represented by the matrix \(P\), and (3) an initial guess \(W_0\) of \(W\), which we base on TF binding motifs. Feel free to play with other choices of \(C\), \(P\), and \(W_0\) as well.

-

OTTER solves the following optimization problem with ADAM gradient descent: \[\min_W \frac{(1-\lambda)}{4}\Vert WW^T - \tilde{P} \Vert^2 + \frac{\lambda}{4} \Vert W^TW - C \Vert^2 + \frac{\gamma}{2} \Vert W \Vert^2,\] which links transformed protein-protein interactions \(\tilde{P} = P+2.2\) and the gene expression correlation matrix \(C\) with the projections of the unknown gene regulatory network \(W\). OTTER infers the gene regulatory network W assuming that P and C are its (noise corrupted) projections.

-

Gradient descent is an iterative optimization method, which needs to be initialized. OTTER starts from \(\tilde{P}W_0\), as this choice defines the current state-of-the-art in gene regulatory network inference based on gene expression data (see the original paper). The transformation \(\tilde{P}W_0\) assumes that binding events to genes with a high number of TF bindings sites are more likely. More details can be found in the original paper.

+
+

OTTER +

+
+

Background of OTTER +

+

OTTER (Optimize to Estimate Regulation) is a general method to infer +a bipartite network \(W\) from noisy +observations of its projections \(WW^T\) and \(W^TW\). It is explained in detail in the +accompanying publication https://www.biorxiv.org/content/10.1101/2020.06.23.167999v2. +In this tutorial, we are particularly interested in constructing a gene +regulatory network \(W\) between +transcription factors (TFs) and genes. Higher link weights are +associated with a higher probability of TFs binding to the promoter +region of a gene. Inference of a bipartite gene regulatory network between transcription factors (TFs) and genes.

+

OTTER requires the following inputs: (1) a correlation matrix \(C\), which is based on gene expression +data, (2) protein-protein interactions represented by the matrix \(P\), and (3) an initial guess \(W_0\) of \(W\), which we base on TF binding motifs. +Feel free to play with other choices of \(C\), \(P\), and \(W_0\) as well.

+

OTTER solves the following optimization problem with ADAM gradient +descent: \[\min_W \frac{(1-\lambda)}{4}\Vert +WW^T - \tilde{P} \Vert^2 + \frac{\lambda}{4} \Vert W^TW - C \Vert^2 + +\frac{\gamma}{2} \Vert W \Vert^2,\] which links transformed +protein-protein interactions \(\tilde{P} = +P+2.2\) and the gene expression correlation matrix \(C\) with the projections of the unknown +gene regulatory network \(W\). OTTER infers the gene regulatory network W assuming that P and C are its (noise corrupted) projections.

+

Gradient descent is an iterative optimization method, which needs to +be initialized. OTTER starts from \(\tilde{P}W_0\), as this choice defines the +current state-of-the-art in gene regulatory network inference based on +gene expression data (see the original paper). The transformation \(\tilde{P}W_0\) assumes that binding events +to genes with a high number of TF bindings sites are more likely. More +details can be found in the original paper.

-
-

-OTTER parameters

-

The success of OTTER depends greatly on the right choice of parameters. Some parameters are related to the OTTER objective, while others refer to the ADAM gradient descent approach. If we want to call OTTER with the original parameters that have been tuned to infer gene regulatory networks for breast cancer and cervix cancer tissues (see original paper), we can simply call the otter function as \(W <- otter(W_0,P,C)\). This sets the original parameters to \(\lambda = 0.0035, \gamma = 0.335, Iter = 32, \eta = 0.00001, bexp = 1\), where \(\lambda \in [0,1]\) is a tuning parameter in the OTTER objective that decides how much importance we give to matching \(C\) or \(\tilde{P}\). \(\gamma \geq 0\) is a regularization parameter in the OTTER objective, which corrects for high noise in \(\tilde{P}\) and \(C\). The parameters \(Iter = 32, \eta = 0.00001, bexp = 1\) refer to the gradient descent procedure, where \(Iter\) controls the number of gradient steps, \(\eta\) the stepsize, and \(bexp\) the exponential decay of the stepsize. In the examples that we study next, we will only use the default parameters for simplicity.

+
+

OTTER parameters +

+

The success of OTTER depends greatly on the right choice of +parameters. Some parameters are related to the OTTER objective, while +others refer to the ADAM gradient descent approach. If we want to call +OTTER with the original parameters that have been tuned to infer gene +regulatory networks for breast cancer and cervix cancer tissues (see +original paper), we can simply call the otter function as \(W <- otter(W_0,P,C)\). This sets the +original parameters to \(\lambda = 0.0035, +\gamma = 0.335, Iter = 32, \eta = 0.00001, bexp = 1\), where +\(\lambda \in [0,1]\) is a tuning +parameter in the OTTER objective that decides how much importance we +give to matching \(C\) or \(\tilde{P}\). \(\gamma \geq 0\) is a regularization +parameter in the OTTER objective, which corrects for high noise in \(\tilde{P}\) and \(C\). The parameters \(Iter = 32, \eta = 0.00001, bexp = 1\) refer +to the gradient descent procedure, where \(Iter\) controls the number of gradient +steps, \(\eta\) the stepsize, and \(bexp\) the exponential decay of the +stepsize. In the examples that we study next, we will only use the +default parameters for simplicity.

-
-

-OTTER Network Inference

-

First, we have to define the input matrices \(P\), \(C\), and \(W_0\). Let’s start with \(P\) and \(W_0\) and locate our ppi data (to construct P) and motif priors (for W_0). The ppi represents physical interactions between transcription factor proteins, and is an undirected network. The transcription factor motif prior represents putative regulation events where a transcription factor binds in the promotor of a gene to regulate its expression, as predicted by the presence of transcription factor binding motifs in the promotor region of the gene. The motif prior is thus a directed bipartite network linking transcription factors to their predicted gene targets. These are small example priors for the purposes of demonstrating this method. A complete set of priors by species can be downloaded from: https://sites.google.com/a/channing.harvard.edu/kimberlyglass/tools/resources

+
+

OTTER Network Inference +

+

First, we have to define the input matrices \(P\), \(C\), and \(W_0\). Let’s start with \(P\) and \(W_0\) and locate our ppi data (to construct +P) and motif priors (for W_0). The ppi represents physical interactions +between transcription factor proteins, and is an undirected network. The +transcription factor motif prior represents putative regulation events +where a transcription factor binds in the promotor of a gene to regulate +its expression, as predicted by the presence of transcription factor +binding motifs in the promotor region of the gene. The motif prior is +thus a directed bipartite network linking transcription factors to their +predicted gene targets. These are small example priors for the purposes +of demonstrating this method. A complete set of priors by species can be +downloaded from: https://sites.google.com/a/channing.harvard.edu/kimberlyglass/tools/resources

Let’s take a look at the priors:

-# download motif and ppi file from AWS Bucket
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/motif_subset.txt")
-system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/ppi_subset.txt")
-motif <- read.delim("./motif_subset.txt", stringsAsFactors=F, header=F)
-ppi <- read.delim("./ppi_subset.txt", stringsAsFactors=F, header=F)
-ppi[1:5,]
-#>      V1      V2    V3
-#> 1 ASCL1   ASCL1 1.000
-#> 2 ASCL1 BHLHE41 0.314
-#> 3 ASCL1    DLX3 0.188
-#> 4 ASCL1     EN2 0.790
-#> 5 ASCL1    HES4 0.451
-motif[1:5,]
-#>      V1     V2 V3
-#> 1 ASCL1  RPAP3  0
-#> 2 ASCL1  ACSM3  0
-#> 3 ASCL1 CREBBP  0
-#> 4 ASCL1 ZNF263  0
-#> 5 ASCL1   RALA  0
-print(dim(ppi))
-#> [1] 430   3
-print(dim(motif))
-#> [1] 49950     3
-

Next we locate our expression data and filter out genes, which are not expressed in too many samples. As example, we will use a subset of the GTEx version 7 RNA-Seq data, downloaded from https://gtexportal.org/home/datasets. We start with a subset of RNA-Seq data (tpm normalized) for 1,000 genes from 130 LCL cell line samples and 407 whole blood samples.

+# download motif and ppi file from AWS Bucket +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/motif_subset.txt") +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/ppi_subset.txt") +motif <- read.delim("./motif_subset.txt", stringsAsFactors=F, header=F) +ppi <- read.delim("./ppi_subset.txt", stringsAsFactors=F, header=F) +ppi[1:5,] +#> V1 V2 V3 +#> 1 ASCL1 ASCL1 1.000 +#> 2 ASCL1 BHLHE41 0.314 +#> 3 ASCL1 DLX3 0.188 +#> 4 ASCL1 EN2 0.790 +#> 5 ASCL1 HES4 0.451 +motif[1:5,] +#> V1 V2 V3 +#> 1 ASCL1 RPAP3 0 +#> 2 ASCL1 ACSM3 0 +#> 3 ASCL1 CREBBP 0 +#> 4 ASCL1 ZNF263 0 +#> 5 ASCL1 RALA 0 +print(dim(ppi)) +#> [1] 430 3 +print(dim(motif)) +#> [1] 49950 3
+

Next we locate our expression data and filter out genes, which are +not expressed in too many samples. As example, we will use a subset of +the GTEx version 7 RNA-Seq data, downloaded from https://gtexportal.org/home/datasets. We start with a +subset of RNA-Seq data (tpm normalized) for 1,000 genes from 130 LCL +cell line samples and 407 whole blood samples.

-# dowload and load the GTEx expression matrix (tpm normalized expression)
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/expression_tpm_lcl_blood_subset.txt")
-exp <- read.delim("./expression_tpm_lcl_blood_subset.txt", stringsAsFactors = F, check.names = F)
-# Log transform the tpm normalized expression
-exp <- log2(exp+1)
-# Determine the number of non-NA/non-zero rows in the expression data. This ensures that we have enough values in the vectors to calculate pearson correlations between gene expression profiles in the construction of the gene co-expression prior.
-zero_na_counts <- apply(exp, MARGIN = 1, FUN = function(x) length(x[(!is.na(x) & x!=0) ]))
-# Maintain only genes with at least 20 valid gene expression entries
-exp <- exp[zero_na_counts > 20,]
-# The set of genes in the expression, motif and ppi matrices must be the same
-exp <- exp[rownames(exp) %in% motif$V2,]
-motif_subset <- motif[(motif$V1 %in% rownames(exp)) & (motif$V2 %in% rownames(exp)),]
-ppi_subset <- ppi[(ppi$V1 %in% motif_subset$V1) & (ppi$V2 %in% motif_subset$V1),]
-# Load the sample ids of LCL samples
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/LCL_samples.txt")
-lcl_samples <-fread("./LCL_samples.txt", header = FALSE, data.table=FALSE)
-# Select the columns of the expression matrix corresponding to the LCL samples
-lcl_exp <- exp[,colnames(exp) %in% lcl_samples[,1]]
-# Load the sample ids of whole blood samples
-system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/WholeBlood_samples.txt")
-wblood_samples <-fread("./WholeBlood_samples.txt", header = FALSE, data.table=FALSE)
-# Select the columns of the expression matrix corresponding to the whole blood samples
-wb_exp <- exp[,colnames(exp) %in% wblood_samples[,1]]
-

For use in OTTER, we have to transform the edgelists into matrices and compute correlation matrices

+# dowload and load the GTEx expression matrix (tpm normalized expression) +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/expression_tpm_lcl_blood_subset.txt") +exp <- read.delim("./expression_tpm_lcl_blood_subset.txt", stringsAsFactors = F, check.names = F) +# Log transform the tpm normalized expression +exp <- log2(exp+1) +# Determine the number of non-NA/non-zero rows in the expression data. This ensures that we have enough values in the vectors to calculate pearson correlations between gene expression profiles in the construction of the gene co-expression prior. +zero_na_counts <- apply(exp, MARGIN = 1, FUN = function(x) length(x[(!is.na(x) & x!=0) ])) +# Maintain only genes with at least 20 valid gene expression entries +exp <- exp[zero_na_counts > 20,] +# The set of genes in the expression, motif and ppi matrices must be the same +exp <- exp[rownames(exp) %in% motif$V2,] +motif_subset <- motif[(motif$V1 %in% rownames(exp)) & (motif$V2 %in% rownames(exp)),] +ppi_subset <- ppi[(ppi$V1 %in% motif_subset$V1) & (ppi$V2 %in% motif_subset$V1),] +# Load the sample ids of LCL samples +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/LCL_samples.txt") +lcl_samples <-fread("./LCL_samples.txt", header = FALSE, data.table=FALSE) +# Select the columns of the expression matrix corresponding to the LCL samples +lcl_exp <- exp[,colnames(exp) %in% lcl_samples[,1]] +# Load the sample ids of whole blood samples +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/WholeBlood_samples.txt") +wblood_samples <-fread("./WholeBlood_samples.txt", header = FALSE, data.table=FALSE) +# Select the columns of the expression matrix corresponding to the whole blood samples +wb_exp <- exp[,colnames(exp) %in% wblood_samples[,1]]
+

For use in OTTER, we have to transform the edgelists into matrices +and compute correlation matrices

-#names of genes and transcription factors (tf)
-geneNames <- unique(motif_subset[,2])
-tfNames <- unique(motif_subset[,1])
-#number of genes and tf
-ng <- length(geneNames)
-ntf <- length(tfNames)
-#initial motif prior:
-W0 <- matrix(data=0, nrow = ntf, ncol=ng, dimnames = list(tfNames, geneNames))
-W0[cbind(motif_subset[,1], motif_subset[,2])] <- motif_subset[,3]
-#protein-protein interaction matrix
-P <- matrix(data=0, nrow = ntf, ncol=ntf, dimnames = list(tfNames, tfNames))
-P[cbind(ppi_subset[,1],ppi_subset[,2])] <- ppi_subset[,3]
-#correlation matrices
-C_lcl <- cor(t(lcl_exp))
-C_wb <- cor(t(wb_exp))
-

Now we can run OTTER. We want to generate two gene regulatory networks for comparison, one based on the LCL and one based on the whole blood data. Hence, we also have to run OTTER twice. Note that the matrices \(P\) and \(W_0\) are identical in each run. The input to OTTER only differ in the correlation matrix \(C_{lcl}\) or \(C_{wb}\), respectively.

+#names of genes and transcription factors (tf) +geneNames <- unique(motif_subset[,2]) +tfNames <- unique(motif_subset[,1]) +#number of genes and tf +ng <- length(geneNames) +ntf <- length(tfNames) +#initial motif prior: +W0 <- matrix(data=0, nrow = ntf, ncol=ng, dimnames = list(tfNames, geneNames)) +W0[cbind(motif_subset[,1], motif_subset[,2])] <- motif_subset[,3] +#protein-protein interaction matrix +P <- matrix(data=0, nrow = ntf, ncol=ntf, dimnames = list(tfNames, tfNames)) +P[cbind(ppi_subset[,1],ppi_subset[,2])] <- ppi_subset[,3] +#correlation matrices +C_lcl <- cor(t(lcl_exp)) +C_wb <- cor(t(wb_exp))
+

Now we can run OTTER. We want to generate two gene regulatory +networks for comparison, one based on the LCL and one based on the whole +blood data. Hence, we also have to run OTTER twice. Note that the +matrices \(P\) and \(W_0\) are identical in each run. The input +to OTTER only differ in the correlation matrix \(C_{lcl}\) or \(C_{wb}\), respectively.

-otterLCL <- otter(W0, P, C_lcl)
-otterWB <- otter(W0, P, C_wb)
-

OTTER networks have relatively small weights because of the internal normalization of edges. But the scaling does not matter for the prediction of regulatory links. The higher the edge weight, the higher is the probability that a transcription factor binds to the promoter region of a gene and regulates its expression. For convenience, we multiply all weights with a factor that sets the maximum weight to one.

+otterLCL <- otter(W0, P, C_lcl) +otterWB <- otter(W0, P, C_wb)
+

OTTER networks have relatively small weights because of the internal +normalization of edges. But the scaling does not matter for the +prediction of regulatory links. The higher the edge weight, the higher +is the probability that a transcription factor binds to the promoter +region of a gene and regulates its expression. For convenience, we +multiply all weights with a factor that sets the maximum weight to +one.

-otterLCL <- otterLCL/max(otterLCL)
-otterWB <- otterWB/max(otterWB)
+otterLCL <- otterLCL/max(otterLCL) +otterWB <- otterWB/max(otterWB)
-
-

-Visualizing networks in Cytoscape

-

In this section we will visualize parts of the network using the Cytoscape software. Download Cytoscape from: https://cytoscape.org

-
-

-Preparing data to plot

+
+

Visualizing networks in Cytoscape +

+

In this section we will visualize parts of the network using the +Cytoscape software. Download Cytoscape from: https://cytoscape.org

+
+

Preparing data to plot +

-# We will use the function vis.panda.in.cytoscape to plot a set of nodes and edges on Cytoscape. The input for this function is a data.frame of edges to plot with 4 columns: "tf", "gene", "motif" (TF motif present or not on gene promoter), "force" (edge weight calculated by OTTER).
-lcl_vis <- melt(otterLCL)
-#> Warning in melt(otterLCL): The melt generic in data.table has been passed a
-#> matrix and will attempt to redirect to the relevant reshape2 method; please note
-#> that reshape2 is deprecated, and this redirection is now deprecated as well.
-#> To continue using melt methods from reshape2 while both libraries are attached,
-#> e.g. melt.list, you can prepend the namespace like reshape2::melt(otterLCL). In
-#> the next version, this warning will become an error.
-wb_vis <- melt(otterWB)
-#> Warning in melt(otterWB): The melt generic in data.table has been passed a
-#> matrix and will attempt to redirect to the relevant reshape2 method; please note
-#> that reshape2 is deprecated, and this redirection is now deprecated as well.
-#> To continue using melt methods from reshape2 while both libraries are attached,
-#> e.g. melt.list, you can prepend the namespace like reshape2::melt(otterWB). In
-#> the next version, this warning will become an error.
-lcl_vis <- data.frame("TF"=as.character(lcl_vis[,1]),"Gene"=as.character(lcl_vis[,2]),"Motif"=NA,"Score"=as.numeric(lcl_vis[,3]),stringsAsFactors = FALSE)
-wb_vis <- data.frame("TF"=as.character(wb_vis[,1]),"Gene"=as.character(wb_vis[,2]),"Motif"=NA,"Score"=as.numeric(wb_vis[,3]),stringsAsFactors = FALSE)
-head(lcl_vis)
-#>        TF  Gene Motif        Score
-#> 1   ASCL1 RPAP3    NA -0.025403065
-#> 2   BARX1 RPAP3    NA -0.031608707
-#> 3 BHLHE41 RPAP3    NA -0.025950559
-#> 4   CEBPD RPAP3    NA  0.011696100
-#> 5   CEBPZ RPAP3    NA -0.026022708
-#> 6   CENPB RPAP3    NA -0.003328924
+# We will use the function vis.panda.in.cytoscape to plot a set of nodes and edges on Cytoscape. The input for this function is a data.frame of edges to plot with 4 columns: "tf", "gene", "motif" (TF motif present or not on gene promoter), "force" (edge weight calculated by OTTER). +lcl_vis <- melt(otterLCL) +#> Warning in melt(otterLCL): The melt generic in data.table has been passed a +#> matrix and will attempt to redirect to the relevant reshape2 method; please +#> note that reshape2 is deprecated, and this redirection is now deprecated as +#> well. To continue using melt methods from reshape2 while both libraries are +#> attached, e.g. melt.list, you can prepend the namespace like +#> reshape2::melt(otterLCL). In the next version, this warning will become an +#> error. +wb_vis <- melt(otterWB) +#> Warning in melt(otterWB): The melt generic in data.table has been passed a +#> matrix and will attempt to redirect to the relevant reshape2 method; please +#> note that reshape2 is deprecated, and this redirection is now deprecated as +#> well. To continue using melt methods from reshape2 while both libraries are +#> attached, e.g. melt.list, you can prepend the namespace like +#> reshape2::melt(otterWB). In the next version, this warning will become an +#> error. +lcl_vis <- data.frame("TF"=as.character(lcl_vis[,1]),"Gene"=as.character(lcl_vis[,2]),"Motif"=NA,"Score"=as.numeric(lcl_vis[,3]),stringsAsFactors = FALSE) +wb_vis <- data.frame("TF"=as.character(wb_vis[,1]),"Gene"=as.character(wb_vis[,2]),"Motif"=NA,"Score"=as.numeric(wb_vis[,3]),stringsAsFactors = FALSE) +head(lcl_vis) +#> TF Gene Motif Score +#> 1 ASCL1 RPAP3 NA -0.025403065 +#> 2 BARX1 RPAP3 NA -0.031608707 +#> 3 BHLHE41 RPAP3 NA -0.025950559 +#> 4 CEBPD RPAP3 NA 0.011696100 +#> 5 CEBPZ RPAP3 NA -0.026022708 +#> 6 CENPB RPAP3 NA -0.003328924
-
-

-Plot the 200 highest edge weights

+
+

Plot the 200 highest edge weights +

-n=200 # number of edges to plot 
-top <- order(lcl_vis$Score,decreasing=T)[1:n]
-lcl_vis_top <- lcl_vis[top,]
-# Plot in cytoscape (open the cytoscape software before running this command)
-vis.panda.in.cytoscape(lcl_vis_top, network_name="LCL")
-# Here we will load a customized visual style for our network, in which TF nodes are orange circles, target gene nodes are blue squares, and edges shade and width are the edge weight (likelyhood of regulatory interaction between the TF and gene). You can further customize the network style directly from Cytoscape.
-create.panda.style(style_name="PandaStyle")
+n=200 # number of edges to plot +top <- order(lcl_vis$Score,decreasing=T)[1:n] +lcl_vis_top <- lcl_vis[top,] +# Plot in cytoscape (open the cytoscape software before running this command) +vis.panda.in.cytoscape(lcl_vis_top, network_name="LCL") +# Here we will load a customized visual style for our network, in which TF nodes are orange circles, target gene nodes are blue squares, and edges shade and width are the edge weight (likelyhood of regulatory interaction between the TF and gene). You can further customize the network style directly from Cytoscape. +create.panda.style(style_name="PandaStyle")
-
-

-Plot the top differential edges betwen LCL and WB

+
+

Plot the top differential edges betwen LCL and WB +

-# Select the top differential edge weights betweeen LCL and whole blood
-diffRes <- panda.diff.edges(lcl_vis, wb_vis, condition_name="LCL")
-head(diffRes)
-# Number of differential edges is:
-nrow(diffRes)
-# Select the top differential edges higher in LCL to plot in Cytoscape
-n=200 # number of edges to select from each condition
-diffResLCL <- diffRes[diffRes$LCL=="T",]
-diffResLCL <- diffResLCL[order(diffResLCL$Score,decreasing=TRUE),][1:n,]
-# Select the top differential edges higher in whole blood to plot in Cytoscape
-diffResWB <- diffRes[diffRes$LCL=="F",]
-diffResWB <- diffResWB[order(diffResWB$Score,decreasing=TRUE),][1:n,]
-# Combine top differential edges in LCL and WB to plot in Cytoscape
-diffRes_vis <- rbind(diffResLCL, diffResWB)
-# Plot the network
-# Purple edges indicate higher edge weight in the defined "condition_name" parameter (LCL in our example), and green edges indicate higher edge weight in the other condition (whole blood in our example).
-vis.diff.panda.in.cytoscape(diffRes_vis, condition_name = "LCL", network_name="diff.PANDA")
-# Apply the style to the network
-create.diff.panda.style(style_name="Diff.PandaStyle", condition_name="LCL")
+# Select the top differential edge weights betweeen LCL and whole blood +diffRes <- panda.diff.edges(lcl_vis, wb_vis, condition_name="LCL") +head(diffRes) +# Number of differential edges is: +nrow(diffRes) +# Select the top differential edges higher in LCL to plot in Cytoscape +n=200 # number of edges to select from each condition +diffResLCL <- diffRes[diffRes$LCL=="T",] +diffResLCL <- diffResLCL[order(diffResLCL$Score,decreasing=TRUE),][1:n,] +# Select the top differential edges higher in whole blood to plot in Cytoscape +diffResWB <- diffRes[diffRes$LCL=="F",] +diffResWB <- diffResWB[order(diffResWB$Score,decreasing=TRUE),][1:n,] +# Combine top differential edges in LCL and WB to plot in Cytoscape +diffRes_vis <- rbind(diffResLCL, diffResWB) +# Plot the network +# Purple edges indicate higher edge weight in the defined "condition_name" parameter (LCL in our example), and green edges indicate higher edge weight in the other condition (whole blood in our example). +vis.diff.panda.in.cytoscape(diffRes_vis, condition_name = "LCL", network_name="diff.PANDA") +# Apply the style to the network +create.diff.panda.style(style_name="Diff.PandaStyle", condition_name="LCL")
-
-

-Calculating degree

+
+

Calculating degree +

    -
  • out-degrees of TFs: sum of the weights of edges pointing to a TF
    • -
  • in-degrees of genes: sum of the weights of edges pointing to a gene
    • +
  • out-degrees of TFs: sum of the weights of edges pointing to a +TF
    • +
  • in-degrees of genes: sum of the weights of edges pointing to a +gene
-lcl_outdegree <- apply(otterLCL, 1, sum) 
-wb_outdegree <- apply(otterWB, 1, sum) 
-lcl_indegree <-  apply(otterLCL, 2, sum) 
-wb_indegree <- apply(otterWB, 2, sum) 
-degreeDiff <- lcl_indegree-wb_indegree 
-head(degreeDiff)
-#>        RPAP3        ACSM3       CREBBP       ZNF263         RALA        PSMB1 
-#> -0.006887092 -0.001475058  0.002100493  0.005429057 -0.017758478 -0.048606106
+lcl_outdegree <- apply(otterLCL, 1, sum) +wb_outdegree <- apply(otterWB, 1, sum) +lcl_indegree <- apply(otterLCL, 2, sum) +wb_indegree <- apply(otterWB, 2, sum) +degreeDiff <- lcl_indegree-wb_indegree +head(degreeDiff) +#> RPAP3 ACSM3 CREBBP ZNF263 RALA PSMB1 +#> -0.006887092 -0.001475058 0.002100493 0.005429057 -0.017758478 -0.048606106
-
-

-Gene Set Enrichment Analysis

-

Well will use the fgsea package to perform a gene set enrichment analysis. Inputs are a ranked gene list (for example the gene in-degree difference between LCL and whole blood) and a list of gene sets (or signatures) in gmt format to test for enrichment. The gene sets can be downloaded from MSigDB: http://software.broadinstitute.org/gsea/msigdb The same gene annotation should be used in the ranked gene list and the gene sets. In our example, we will use the KEGG pathways downloaded from MSigDB.

-
-

-Run fgsea

+
+

Gene Set Enrichment Analysis +

+

Well will use the fgsea package to perform a gene set enrichment +analysis. Inputs are a ranked gene list (for example the gene in-degree +difference between LCL and whole blood) and a list of gene sets (or +signatures) in gmt format to test for enrichment. The gene sets can be +downloaded from MSigDB: http://software.broadinstitute.org/gsea/msigdb The same +gene annotation should be used in the ranked gene list and the gene +sets. In our example, we will use the KEGG pathways downloaded from +MSigDB.

+
+

Run fgsea +

-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/c2.cp.kegg.v7.0.symbols.gmt")
-pathways <- gmtPathways("./c2.cp.kegg.v7.0.symbols.gmt")
-# To retrieve biological-relevant processes, we will load and use the complete ranked gene list (consisting of 26,077 out of 27,174 genes). The in-degree difference has been calculated based on the complete networks instead of the subnetworks, which we constructed in this tutorial as small examples (with reduced run time).
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/lclWB_indegreeDifference_otter.txt")
-degreeDiff_all <- read.delim("./lclWB_indegreeDifference_otter.txt",stringsAsFactors = F,header=F)
-degreeDiff_all <- setNames(degreeDiff_all[,2], degreeDiff_all[,1])
-fgseaRes <- fgsea(pathways, degreeDiff_all, minSize=15, maxSize=500, nperm=1000)
-head(fgseaRes)
-#>                                          pathway        pval       padj
-#> 1:               KEGG_GLYCOLYSIS_GLUCONEOGENESIS 0.002028398 0.01741098
-#> 2:                  KEGG_CITRATE_CYCLE_TCA_CYCLE 0.217131474 0.36256859
-#> 3:                KEGG_PENTOSE_PHOSPHATE_PATHWAY 0.083168317 0.17735894
-#> 4: KEGG_PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS 0.390873016 0.56708626
-#> 5:          KEGG_FRUCTOSE_AND_MANNOSE_METABOLISM 0.036734694 0.10659083
-#> 6:                     KEGG_GALACTOSE_METABOLISM 0.329243354 0.49808610
-#>            ES       NES nMoreExtreme size                          leadingEdge
-#> 1:  0.4360719  1.779675            0   58    HK1,ENO2,ENO1,ALDOC,BPGM,PFKM,...
-#> 2: -0.3428560 -1.172256          108   29  DLST,PCK2,SUCLG2P2,MDH2,FH,IDH2,...
-#> 3:  0.4435710  1.414328           41   21   ALDOC,PFKM,PFKP,ALDOA,PFKL,GPI,...
-#> 4:  0.3180477  1.031980          196   22   UGT1A1,UGT1A10,UGT1A6,CRYL1,UGT2B7
-#> 5:  0.4118882  1.458885           17   31 HK1,ALDOC,PFKFB4,PFKM,MTMR2,PFKP,...
-#> 6:  0.3247973  1.088178          160   25      GLB1,HK1,PFKM,PFKP,PFKL,GAA,...
-# Subset to pathways with FDR < 0.05
-sig <- fgseaRes[fgseaRes$padj < 0.05,]
-# Get the top 10 significant pathways enriched for genes having lower targeting in LCLs  
-sig[order(sig$NES)[1:10],]
-#>                             pathway        pval       padj         ES       NES
-#>  1:            KEGG_DNA_REPLICATION 0.001901141 0.01741098 -0.7725018 -2.758721
-#>  2:                 KEGG_CELL_CYCLE 0.001773050 0.01741098 -0.5948628 -2.744328
-#>  3:                KEGG_SPLICEOSOME 0.001795332 0.01741098 -0.5766563 -2.635589
-#>  4:             KEGG_PROTEIN_EXPORT 0.001980198 0.01741098 -0.6851677 -2.242257
-#>  5: KEGG_NUCLEOTIDE_EXCISION_REPAIR 0.001919386 0.01741098 -0.5785505 -2.157588
-#>  6:       KEGG_BASE_EXCISION_REPAIR 0.001934236 0.01741098 -0.5965607 -2.107505
-#>  7:      KEGG_PYRIMIDINE_METABOLISM 0.001841621 0.01741098 -0.4606911 -2.012965
-#>  8:            KEGG_MISMATCH_REPAIR 0.002008032 0.01741098 -0.6107380 -1.970065
-#>  9:             KEGG_RNA_POLYMERASE 0.001992032 0.01741098 -0.5731001 -1.955036
-#> 10:      KEGG_N_GLYCAN_BIOSYNTHESIS 0.003891051 0.02463808 -0.4815544 -1.828574
-#>     nMoreExtreme size                                    leadingEdge
-#>  1:            0   34             RFC5,RFC3,FEN1,MCM7,POLE2,DNA2,...
-#>  2:            0  121          ORC6,CDC7,CDC20,PKMYT1,CCNE1,ORC1,...
-#>  3:            0  118          PHF5A,SRSF3,LSM7,SRSF7,LSM4,U2AF2,...
-#>  4:            0   23       IMMP1L,SRP9,SRPRB,SPCS3,HSPA5,IMMP2L,...
-#>  5:            0   41             RFC5,RFC3,POLE2,CCNH,RFC4,PCNA,...
-#>  6:            0   33           FEN1,HMGB1,MUTYH,UNG,POLE2,NEIL3,...
-#>  7:            0   90         POLR2D,DUT,POLE2,PRIM1,DTYMK,DHODH,...
-#>  8:            0   22              RFC5,RFC3,RFC4,PCNA,EXO1,RFC2,...
-#>  9:            0   28 POLR2D,POLR3B,POLR2H,POLR2L,POLR3GL,POLR2J,...
-#> 10:            1   45         B4GALT2,DPM3,ALG14,FUT8,ALG6,MGAT2,...
+system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/c2.cp.kegg.v7.0.symbols.gmt") +pathways <- gmtPathways("./c2.cp.kegg.v7.0.symbols.gmt") +# To retrieve biological-relevant processes, we will load and use the complete ranked gene list (consisting of 26,077 out of 27,174 genes). The in-degree difference has been calculated based on the complete networks instead of the subnetworks, which we constructed in this tutorial as small examples (with reduced run time). +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/lclWB_indegreeDifference_otter.txt") +degreeDiff_all <- read.delim("./lclWB_indegreeDifference_otter.txt",stringsAsFactors = F,header=F) +degreeDiff_all <- setNames(degreeDiff_all[,2], degreeDiff_all[,1]) +fgseaRes <- fgsea(pathways, degreeDiff_all, minSize=15, maxSize=500, nperm=1000) +head(fgseaRes) +#> pathway pval padj +#> 1: KEGG_GLYCOLYSIS_GLUCONEOGENESIS 0.002227171 0.01490903 +#> 2: KEGG_CITRATE_CYCLE_TCA_CYCLE 0.239622642 0.39271488 +#> 3: KEGG_PENTOSE_PHOSPHATE_PATHWAY 0.065306122 0.14935489 +#> 4: KEGG_PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS 0.421711900 0.60195973 +#> 5: KEGG_FRUCTOSE_AND_MANNOSE_METABOLISM 0.047210300 0.12769145 +#> 6: KEGG_GALACTOSE_METABOLISM 0.329875519 0.50216798 +#> ES NES nMoreExtreme size leadingEdge +#> 1: 0.4360719 1.734843 0 58 HK1,ENO2,ENO1,ALDOC,BPGM,PFKM,... +#> 2: -0.3428560 -1.165893 126 29 DLST,PCK2,SUCLG2P2,MDH2,FH,IDH2,... +#> 3: 0.4435710 1.426434 31 21 ALDOC,PFKM,PFKP,ALDOA,PFKL,GPI,... +#> 4: 0.3180477 1.030365 201 22 UGT1A1,UGT1A10,UGT1A6,CRYL1,UGT2B7 +#> 5: 0.4118882 1.445175 21 31 HK1,ALDOC,PFKFB4,PFKM,MTMR2,PFKP,... +#> 6: 0.3247973 1.081608 158 25 GLB1,HK1,PFKM,PFKP,PFKL,GAA,... +# Subset to pathways with FDR < 0.05 +sig <- fgseaRes[fgseaRes$padj < 0.05,] +# Get the top 10 significant pathways enriched for genes having lower targeting in LCLs +sig[order(sig$NES)[1:10],] +#> pathway pval padj ES NES +#> 1: KEGG_DNA_REPLICATION 0.001879699 0.01490903 -0.7725018 -2.751185 +#> 2: KEGG_CELL_CYCLE 0.001858736 0.01490903 -0.5948628 -2.734800 +#> 3: KEGG_SPLICEOSOME 0.001851852 0.01490903 -0.5766563 -2.641762 +#> 4: KEGG_PROTEIN_EXPORT 0.001926782 0.01490903 -0.6851677 -2.189553 +#> 5: KEGG_NUCLEOTIDE_EXCISION_REPAIR 0.001912046 0.01490903 -0.5785505 -2.133075 +#> 6: KEGG_BASE_EXCISION_REPAIR 0.001876173 0.01490903 -0.5965607 -2.105490 +#> 7: KEGG_PYRIMIDINE_METABOLISM 0.001851852 0.01490903 -0.4606911 -2.013456 +#> 8: KEGG_MISMATCH_REPAIR 0.001912046 0.01490903 -0.6107380 -1.941010 +#> 9: KEGG_RNA_POLYMERASE 0.003824092 0.02183433 -0.5731001 -1.924917 +#> 10: KEGG_N_GLYCAN_BIOSYNTHESIS 0.001915709 0.01490903 -0.4815544 -1.818604 +#> nMoreExtreme size leadingEdge +#> 1: 0 34 RFC5,RFC3,FEN1,MCM7,POLE2,DNA2,... +#> 2: 0 121 ORC6,CDC7,CDC20,PKMYT1,CCNE1,ORC1,... +#> 3: 0 118 PHF5A,SRSF3,LSM7,SRSF7,LSM4,U2AF2,... +#> 4: 0 23 IMMP1L,SRP9,SRPRB,SPCS3,HSPA5,IMMP2L,... +#> 5: 0 41 RFC5,RFC3,POLE2,CCNH,RFC4,PCNA,... +#> 6: 0 33 FEN1,HMGB1,MUTYH,UNG,POLE2,NEIL3,... +#> 7: 0 90 POLR2D,DUT,POLE2,PRIM1,DTYMK,DHODH,... +#> 8: 0 22 RFC5,RFC3,RFC4,PCNA,EXO1,RFC2,... +#> 9: 1 28 POLR2D,POLR3B,POLR2H,POLR2L,POLR3GL,POLR2J,... +#> 10: 0 45 B4GALT2,DPM3,ALG14,FUT8,ALG6,MGAT2,...
-
-

-Bubble plot of top differentially targeted pathways

-

Bubble plot of gene sets (KEGG pathways) on y-axis and adjusted p-value (padj) on x-axis. Bubble size indicates the number of genes in each gene set, and bubble color indicates the normalized enrichment score (NES). Blue is for negative NES (enrichment of higher targeted genes in whole blood), and red is for positive NES (enrichment of higher targeted genes in LCL).

+
+

Bubble plot of top differentially targeted pathways +

+

Bubble plot of gene sets (KEGG pathways) on y-axis and adjusted +p-value (padj) on x-axis. Bubble size indicates the number of genes in +each gene set, and bubble color indicates the normalized enrichment +score (NES). Blue is for negative NES (enrichment of higher targeted +genes in whole blood), and red is for positive NES (enrichment of higher +targeted genes in LCL).

-dat <- data.frame(fgseaRes)
-# Settings
-fdrcut <- 0.05 # FDR cut-off to use as output for significant signatures
-dencol_neg <- "blue" # bubble plot color for negative ES
-dencol_pos <- "red" # bubble plot color for positive ES
-signnamelength <- 4 # set to remove prefix from signature names (2 for "GO", 4 for "KEGG", 8 for "REACTOME")
-asp <- 3 # aspect ratio of bubble plot
-charcut <- 100 # cut signature name in heatmap to this nr of characters
-# Make signature names more readable
-a <- as.character(dat$pathway) # 'a' is a great variable name to substitute row names with something more readable
-for (j in 1:length(a)){
-  a[j] <- substr(a[j], signnamelength+2, nchar(a[j]))
-}
-a <- tolower(a) # convert to lower case (you may want to comment this out, it really depends on what signatures you are looking at, c6 signatures contain gene names, and converting those to lower case may be confusing)
-for (j in 1:length(a)){
-  if(nchar(a[j])>charcut) { a[j] <- paste(substr(a[j], 1, charcut), "...", sep=" ")}
-} # cut signature names that have more characters than charcut, and add "..."
-a <- gsub("_", " ", a)
-dat$NAME <- a
-# Determine what signatures to plot (based on FDR cut)
-dat2 <- dat[dat[,"padj"]<fdrcut,]
-dat2 <- dat2[order(dat2[,"padj"]),] 
-dat2$signature <- factor(dat2$NAME, rev(as.character(dat2$NAME)))
-# Determine what labels to color
-sign_neg <- which(dat2[,"NES"]<0)
-sign_pos <- which(dat2[,"NES"]>0)
-# Color labels
-signcol <- rep(NA, length(dat2$signature))
-signcol[sign_neg] <- dencol_neg # text color of negative signatures
-signcol[sign_pos] <- dencol_pos # text color of positive signatures
-signcol <- rev(signcol) # need to revert vector of colors, because ggplot starts plotting these from below
-# Plot bubble plot
-g<-ggplot(dat2, aes(x=padj,y=signature,size=size))
-g+geom_point(aes(fill=NES), shape=21, colour="white")+
-  theme_bw()+ # white background, needs to be placed before the "signcol" line
-  xlim(0,fdrcut)+
-  scale_size_area(max_size=10,guide="none")+
-  scale_fill_gradient2(low=dencol_neg, high=dencol_pos)+
-  theme(axis.text.y = element_text(colour=signcol))+
-  theme(aspect.ratio=asp, axis.title.y=element_blank()) # test aspect.ratio
-#> Warning: Vectorized input to `element_text()` is not officially supported.
-#> Results may be unexpected or may change in future versions of ggplot2.
+dat <- data.frame(fgseaRes) +# Settings +fdrcut <- 0.05 # FDR cut-off to use as output for significant signatures +dencol_neg <- "blue" # bubble plot color for negative ES +dencol_pos <- "red" # bubble plot color for positive ES +signnamelength <- 4 # set to remove prefix from signature names (2 for "GO", 4 for "KEGG", 8 for "REACTOME") +asp <- 3 # aspect ratio of bubble plot +charcut <- 100 # cut signature name in heatmap to this nr of characters +# Make signature names more readable +a <- as.character(dat$pathway) # 'a' is a great variable name to substitute row names with something more readable +for (j in 1:length(a)){ + a[j] <- substr(a[j], signnamelength+2, nchar(a[j])) +} +a <- tolower(a) # convert to lower case (you may want to comment this out, it really depends on what signatures you are looking at, c6 signatures contain gene names, and converting those to lower case may be confusing) +for (j in 1:length(a)){ + if(nchar(a[j])>charcut) { a[j] <- paste(substr(a[j], 1, charcut), "...", sep=" ")} +} # cut signature names that have more characters than charcut, and add "..." +a <- gsub("_", " ", a) +dat$NAME <- a +# Determine what signatures to plot (based on FDR cut) +dat2 <- dat[dat[,"padj"]<fdrcut,] +dat2 <- dat2[order(dat2[,"padj"]),] +dat2$signature <- factor(dat2$NAME, rev(as.character(dat2$NAME))) +# Determine what labels to color +sign_neg <- which(dat2[,"NES"]<0) +sign_pos <- which(dat2[,"NES"]>0) +# Color labels +signcol <- rep(NA, length(dat2$signature)) +signcol[sign_neg] <- dencol_neg # text color of negative signatures +signcol[sign_pos] <- dencol_pos # text color of positive signatures +signcol <- rev(signcol) # need to revert vector of colors, because ggplot starts plotting these from below +# Plot bubble plot +g<-ggplot(dat2, aes(x=padj,y=signature,size=size)) +g+geom_point(aes(fill=NES), shape=21, colour="white")+ + theme_bw()+ # white background, needs to be placed before the "signcol" line + xlim(0,fdrcut)+ + scale_size_area(max_size=10,guide="none")+ + scale_fill_gradient2(low=dencol_neg, high=dencol_pos)+ + theme(axis.text.y = element_text(colour=signcol))+ + theme(aspect.ratio=asp, axis.title.y=element_blank()) # test aspect.ratio +#> Warning: Vectorized input to `element_text()` is not officially supported. +#> ℹ Results may be unexpected or may change in future versions of ggplot2.

-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/TutorialOTTER_files/figure-html/unnamed-chunk-15-1.png b/docs/articles/TutorialOTTER_files/figure-html/unnamed-chunk-15-1.png index e41fc90a..daedeee0 100644 Binary files a/docs/articles/TutorialOTTER_files/figure-html/unnamed-chunk-15-1.png and b/docs/articles/TutorialOTTER_files/figure-html/unnamed-chunk-15-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index a9cfd584..4adcf110 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -1,214 +1,110 @@ - - - - - - - -Articles • netZooR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Articles • netZooR + Skip to contents + +
-
-
-

Articles

+
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Articles

All vignettes

-
-
Detecting differential modules using ALPACA
-
-
Building PANDA and LIONESS Regulatory Networks from GTEx Gene Expression Data in R
-
-
netZooR application with TB dataset
-
-
Using CONDOR for community detection in bipartite graphs
-
-
Constructing genotype-specific gene regulatory networks with EGRET
-
-
Comparing LIONESS Regulatory Networks using limma
-
-
An Introduction to the MONSTER Package
-
-
SAMBAR: Subtyping Agglomerated Mutations By Annotation Relations
-
-
Inferring Gene Regulatory Networks from GTEx Gene Expression Data in R with OTTER
-
-
An Introduction to the pandaR Package
-
-
Building PANDA Regulatory Networks from GTEx Gene Expression Data in R
-
-
YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization
-
-
-
-
+
Detecting differential modules using ALPACA
+
+
Building PANDA and LIONESS Regulatory Networks from GTEx Gene Expression Data in R
+
+
netZooR application with TB dataset
+
+
Using CONDOR for community detection in bipartite graphs
+
+
Constructing genotype-specific gene regulatory networks with EGRET
+
+
Comparing LIONESS Regulatory Networks using limma
+
+
An Introduction to the MONSTER Package
+
+
SAMBAR: Subtyping Agglomerated Mutations By Annotation Relations
+
+
Inferring Gene Regulatory Networks from GTEx Gene Expression Data in R with OTTER
+
+
An Introduction to the pandaR Package
+
+
Building PANDA Regulatory Networks from GTEx Gene Expression Data in R
+
+
YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization
+
+
+
+ + +
+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

- -
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

Site built with pkgdown 2.0.7.

-
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Site built with pkgdown 1.6.1.

-
- -
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+
+ - - - + diff --git a/docs/articles/pandaR.html b/docs/articles/pandaR.html index b2d6b04f..cd46bd35 100644 --- a/docs/articles/pandaR.html +++ b/docs/articles/pandaR.html @@ -4,282 +4,307 @@ - + + An Introduction to the pandaR Package • netZooR - - - + + + - - + - -
-
-
-
-

An Introduction to the pandaR Package

-

Daniel Schlauch, Albert Young, Joseph N. Paulson

+ + +
+
+

An Introduction to the pandaR Package

+

Daniel +Schlauch, Albert Young, Joseph N. Paulson

-

2016-05-01

+

2016-05-01

- Source: vignettes/pandaR.Rmd - - + Source: vignettes/pandaR.Rmd +
pandaR.Rmd
-
-

-Introduction

-

The fundamental concepts behind the PANDA approach is to model the regulatory network as a bipartite network and estimate edge weights based on the evidence that information from a particular transcription factor i is successfully being passed to a particular gene j. This evidence comes from the agreement between two measured quantities. First, the correlation in expression between gene j and other genes. And second, the strength of evidence of the existence of an edge between TF i and those same genes. This concordance is measured using Tanimoto similarity. A gene is said to be available if there is strong evidence of this type of agreement. Analogous to this is the concept of responsibility which similarly focuses on a TF-gene network edge but instead measures the concordance between suspected protein-complex partners of TF i and the respective strength of evidence of a regulatory pathway between those TFs and gene j.

-

PANDA utilizes an iterative approach to updating the bipartite edge weights incrementally as evidence for new edges emerges and evidence for existing edges diminishes. This process continues until the algorithm reaches a point of convergence settling on a final score for the strength of information supporting a regulatory mechanism for every pairwise combination of TFs and genes. This package provides a straightforward tool for applying this established method. Beginning with data.frames or matrices representing a set of gene expression samples, motif priors and optional protein-protein interaction users can generate an m by n matrix representing the bipartite network from m TFs regulating n genes. Additionally, pandaR reports the co-regulation and cooperative networks at convergence. These are reported as complete graphs representing the evidence for gene co-regulation and transcription factor cooperation.

+
+

Introduction +

+

The fundamental concepts behind the PANDA approach is to model the +regulatory network as a bipartite network and estimate edge weights +based on the evidence that information from a particular transcription +factor i is successfully being passed to a particular gene +j. This evidence comes from the agreement between two measured +quantities. First, the correlation in expression between gene j +and other genes. And second, the strength of evidence of the existence +of an edge between TF i and those same genes. This concordance +is measured using Tanimoto similarity. A gene is said to be available if +there is strong evidence of this type of agreement. Analogous to this is +the concept of responsibility which similarly focuses on a TF-gene +network edge but instead measures the concordance between suspected +protein-complex partners of TF i and the respective strength of +evidence of a regulatory pathway between those TFs and gene +j.

+

PANDA utilizes an iterative approach to updating the bipartite edge +weights incrementally as evidence for new edges emerges and evidence for +existing edges diminishes. This process continues until the algorithm +reaches a point of convergence settling on a final score for the +strength of information supporting a regulatory mechanism for every +pairwise combination of TFs and genes. This package provides a +straightforward tool for applying this established method. Beginning +with data.frames or matrices representing a set of gene expression +samples, motif priors and optional protein-protein interaction users can +generate an m by n matrix representing the bipartite +network from m TFs regulating n genes. Additionally, +pandaR reports the co-regulation and cooperative networks at +convergence. These are reported as complete graphs representing the +evidence for gene co-regulation and transcription factor +cooperation.

-
-

-Example

-

An example dataset derived from a subset of stress-induced Yeast is available by running

+
+

Example +

+

An example dataset derived from a subset of stress-induced Yeast is +available by running

-
## Loading required package: igraph
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## Loading required package: reticulate
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## Loading required package: pandaR
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-##     'browseVignettes()'. To cite Bioconductor, see
-##     'citation("Biobase")', and for packages 'citation("pkgname")'.
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## Loading required package: yarn
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## Setting options('download.file.method.GEOquery'='auto')
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+library(netZooR)
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+##     Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
+##     table, tapply, union, unique, unsplit, which.max, which.min
+
## Welcome to Bioconductor
+## 
+##     Vignettes contain introductory material; view with
+##     'browseVignettes()'. To cite Bioconductor, see
+##     'citation("Biobase")', and for packages 'citation("pkgname")'.
+
## Loading required package: yarn
+
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+## Attaching package: 'matrixcalc'
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## 
-library(pandaR)
-data(pandaToyData)
-

pandaToyData is a list containing a regulatory structure derived from sequence motif analysis, protein-protein interaction data and a gene expression. The primary function in pandaR is called with

+library(pandaR) +data(pandaToyData)
+

pandaToyData is a list containing a regulatory +structure derived from sequence motif analysis, protein-protein +interaction data and a gene expression. The primary function in pandaR +is called with

-pandaResult <- panda(pandaToyData$motif, pandaToyData$expression, pandaToyData$ppi)
-pandaResult
-
## PANDA network for1000genes and87transcription factors.
-
## 
-## Slots:
-
## regNet   : Regulatory network of 87 transcription factors to 1000 genes.
-
## coregNet: Co-regulation network of 1000 genes.
-
## coopNet  : Cooperative network of 87 transcription factors.
-
## Regulatory graph contains 87000 edges.
-
## Regulatory graph is complete.
-

Where pandaResult is a ‘panda’ object which contains data.frames describing the complete bipartite gene regulatory network as well as complete networks for gene coregulation and transcription factor cooperation. Due to completeness, edgeweights for the regulatory network are reported for all mxn possible TF-gene edges. The distribution of these edge weights for these networks has approximate mean 0 and standard deviation 1. The edges are therefore best interpreted in a relative sense. Strongly positive values indicative of relatively larger amounts of evidence in favor a regulatory mechanism and conversely, smaller or negative values can be interpreted as lacking evidence of a shared biological role. It is naturally of interest to specify a high edge weight subset of the complete network to investigate as a set of present/absent edges. This is easily performed by using the topedges function. A network containing the top 1000 edge scores as binary edges can be obtained by:

+pandaResult <- panda(pandaToyData$motif, pandaToyData$expression, pandaToyData$ppi) +pandaResult
+
## PANDA network for1000genes and87transcription factors.
+
## 
+## Slots:
+
## regNet   : Regulatory network of 87 transcription factors to 1000 genes.
+
## coregNet: Co-regulation network of 1000 genes.
+
## coopNet  : Cooperative network of 87 transcription factors.
+
## Regulatory graph contains 87000 edges.
+
## Regulatory graph is complete.
+

Where pandaResult is a ‘panda’ object which contains +data.frames describing the complete bipartite gene +regulatory network as well as complete networks for gene coregulation +and transcription factor cooperation. Due to completeness, edgeweights +for the regulatory network are reported for all mxn +possible TF-gene edges. The distribution of these edge weights for these +networks has approximate mean 0 and standard deviation 1. The edges are +therefore best interpreted in a relative sense. Strongly positive values +indicative of relatively larger amounts of evidence in favor a +regulatory mechanism and conversely, smaller or negative values can be +interpreted as lacking evidence of a shared biological role. It is +naturally of interest to specify a high edge weight subset of the +complete network to investigate as a set of present/absent edges. This +is easily performed by using the topedges function. A +network containing the top 1000 edge scores as binary edges can be +obtained by:

-topNet <- topedges(pandaResult, 1000)
-

Users may then examine the genes targeted by a transcription factor of interest.

+topNet <- topedges(pandaResult, 1000)
+

Users may then examine the genes targeted by a transcription factor +of interest.

-targetedGenes(topNet, c("AR"))
-
##  [1] "AKAP10"       "CNDP2"        "CRHR1"        "HNRNPD"       "KIAA0652"    
-##  [6] "LOC100093631" "LOC100128811" "PRR15"        "TCF4"         "TCP11L2"     
-## [11] "TMPRSS11B"    "VCX3B"        "WDR4"
-

The network can be further simplified by focusing only on transcription factors on interest and the genes that they are found to regulate. The subnetwork method serves this function

+targetedGenes(topNet, c("AR"))
+
##  [1] "AKAP10"       "CNDP2"        "CRHR1"        "HNRNPD"       "KIAA0652"    
+##  [6] "LOC100093631" "LOC100128811" "PRR15"        "TCF4"         "TCP11L2"     
+## [11] "TMPRSS11B"    "VCX3B"        "WDR4"
+

The network can be further simplified by focusing only on +transcription factors on interest and the genes that they are found to +regulate. The subnetwork method serves this +function

-topSubnet <- subnetwork(topNet, c("AR","ARID3A","ELK1"))
-

Existing R packages, such as igraph, can be used to visualize the results

+topSubnet <- subnetwork(topNet, c("AR","ARID3A","ELK1"))
+

Existing R packages, such as igraph, can be used to visualize the +results

-plotGraph(topSubnet)
+plotGraph(topSubnet)

-
-

-Comparing state-specific PANDA networks

-

We provide a number of useful plotting functions for the analysis of the networks. The main functions used to plot and analyze the PANDA networks are:

+
+

Comparing state-specific PANDA networks +

+

We provide a number of useful plotting functions for the analysis of +the networks. The main functions used to plot and analyze the PANDA +networks are:

  • plotZ -Comparison of Z scores between two PANDA runs
  • plotZbyTF - Plot Z by TF outdegree quartiles
-

We can compare how parameter choices effect the Z-score estimation between two PANDA runs. Additionally, we can compare two phenotypes:

+

We can compare how parameter choices effect the Z-score estimation +between two PANDA runs. Additionally, we can compare two phenotypes:

-panda.res1 <- with(pandaToyData, panda(motif, expression, ppi, hamming=1))
-panda.res2 <- with(pandaToyData, panda(motif, expression + 
-                   rnorm(prod(dim(expression)),sd=5), ppi, hamming=1))
-plotZ(panda.res1, panda.res2,addLine=FALSE)
-
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
-## caller; using TRUE
-
-## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
-## caller; using TRUE
-
-## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
-## caller; using TRUE
-
-## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
-## caller; using TRUE
+panda.res1 <- with(pandaToyData, panda(motif, expression, ppi, hamming=1)) +panda.res2 <- with(pandaToyData, panda(motif, expression + + rnorm(prod(dim(expression)),sd=5), ppi, hamming=1)) +plotZ(panda.res1, panda.res2,addLine=FALSE)
+
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
+## the caller; using TRUE
+
+## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
+## the caller; using TRUE
+
+## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
+## the caller; using TRUE
+
+## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
+## the caller; using TRUE

Other comparisons are underway…

-
-

-Other helpful functions

-

There are a host of other helpful functions, including testMotif, plotCommunityDetection, and multiplot. See the full help pages.

+
+

Other helpful functions +

+

There are a host of other helpful functions, including +testMotif, plotCommunityDetection, and +multiplot. See the full help pages.

##References

-

Glass K, Huttenhower C, Quackenbush J, Yuan GC. Passing Messages Between Biological Networks to Refine Predicted Interactions, PLoS One, 2013 May 31;8(5):e64832

-

Glass K, Quackenbush J, Silverman EK, Celli B, Rennard S, Yuan GC and DeMeo DL. Sexually-dimorphic targeting of functionally-related genes in COPD, BMC Systems Biology, 2014 Nov 28; 8:118

-
-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/pandaRApplicationinGTExData.html b/docs/articles/pandaRApplicationinGTExData.html index 908c5778..03c788bb 100644 --- a/docs/articles/pandaRApplicationinGTExData.html +++ b/docs/articles/pandaRApplicationinGTExData.html @@ -4,468 +4,487 @@ - + + Building PANDA Regulatory Networks from GTEx Gene Expression Data in R • netZooR - - - + + + - - + - -
-
-
-
-

Building PANDA Regulatory Networks from GTEx Gene Expression Data in R

-

Camila Lopes-Ramos

+ + +
+
+

Building PANDA Regulatory Networks from GTEx Gene Expression Data in R

+

Camila +Lopes-Ramos

-

2020-10-19

+

2020-10-19

- Source: vignettes/pandaRApplicationinGTExData.Rmd - - + Source: vignettes/pandaRApplicationinGTExData.Rmd +
pandaRApplicationinGTExData.Rmd
-
-

-Introduction

-

In this vignette, we will build one regulatory network for LCL cell line samples and one for whole blood samples from the GTEx gene expression data using the netZooR package. Next, we will compare the two networks, and find the pathways enriched for genes differentially targeted between the LCL cell line and whole blood.

-

Cell lines are an essential tool in biomedical research and often used as surrogates for tissues. LCLs (obtained from the transformation of B cells present in whole blood) are among the most widely used continuous cell lines with the ability to proliferate indefinitely. By comparing the regulatory networks of LCL cell lines with its tissue of origin (whole blood), we find that LCLs exhibit large changes in their patterns of transcription factor regulation, specifically a loss of repressive transcription factor targeting of cell cycle genes.

-
-

-Install packages

+
+

Introduction +

+

In this vignette, we will build one regulatory network for LCL cell +line samples and one for whole blood samples from the GTEx gene +expression data using the netZooR package. Next, we will compare the two +networks, and find the pathways enriched for genes differentially +targeted between the LCL cell line and whole blood.

+

Cell lines are an essential tool in biomedical research and often +used as surrogates for tissues. LCLs (obtained from the transformation +of B cells present in whole blood) are among the most widely used +continuous cell lines with the ability to proliferate indefinitely. By +comparing the regulatory networks of LCL cell lines with its tissue of +origin (whole blood), we find that LCLs exhibit large changes in their +patterns of transcription factor regulation, specifically a loss of +repressive transcription factor targeting of cell cycle genes.

+
+

Install packages +

-if (!requireNamespace("BiocManager", quietly = TRUE))   
-    install.packages("BiocManager",repos = "http://cran.us.r-project.org")  
-BiocManager::install("fgsea")   
-# install.packages("reshape2",repos = "http://cran.us.r-project.org")   
-install.packages("ggplot2",repos = "http://cran.us.r-project.org")
+if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager",repos = "http://cran.us.r-project.org") +BiocManager::install("fgsea") +# install.packages("reshape2",repos = "http://cran.us.r-project.org") +install.packages("ggplot2",repos = "http://cran.us.r-project.org") 
-install.packages("devtools")    
-library(devtools)
-devtools::install_github("netZoo/netZooR", build_vignettes = FALSE)
+install.packages("devtools") +library(devtools) +devtools::install_github("netZoo/netZooR", build_vignettes = FALSE)
-
-
-

-PANDA

-
-

-PANDA Overview

-

PANDA (Passing Attributes between Networks for Data Assimilation) is a method for constructing gene regulatory networks. It uses message passing to find congruence between 3 different data layers: protein-protein interaction (PPI), gene expression, and transcription factor (TF) motif data.

-

More details can be found in the published paper https://doi.org/10.1371/journal.pone.0064832.

+
+

PANDA +

+
+

PANDA Overview +

+

PANDA (Passing Attributes between Networks for Data Assimilation) is +a method for constructing gene regulatory networks. It uses message +passing to find congruence between 3 different data layers: +protein-protein interaction (PPI), gene expression, and transcription +factor (TF) motif data.

+

More details can be found in the published paper https://doi.org/10.1371/journal.pone.0064832.

-
-

-Building a PANDA regulatory network

-

Now we locate our ppi and motif priors. The ppi represents physical interactions between transcription factor proteins, and is an undirected network. The transcription factor motif prior represents putative regulation events where a transcription factor binds in the promotor of a gene to regulate its expression, as predicted by the presence of transcription factor binding motifs in the promotor region of the gene. The motif prior is thus a directed network linking transcription factors to their predicted gene targets. These are small example priors for the purposes of demonstrating this method. A complete set of motif priors by species can be downloaded from: https://sites.google.com/a/channing.harvard.edu/kimberlyglass/tools/resources
-The function source.PPI can be used to source the protein-protein interaction in the STRING database.

+
+

Building a PANDA regulatory network +

+

Now we locate our ppi and motif priors. The ppi represents physical +interactions between transcription factor proteins, and is an undirected +network. The transcription factor motif prior represents putative +regulation events where a transcription factor binds in the promotor of +a gene to regulate its expression, as predicted by the presence of +transcription factor binding motifs in the promotor region of the gene. +The motif prior is thus a directed network linking transcription factors +to their predicted gene targets. These are small example priors for the +purposes of demonstrating this method. A complete set of motif priors by +species can be downloaded from: https://sites.google.com/a/channing.harvard.edu/kimberlyglass/tools/resources
+The function sourcePPI() can be used to source the protein-protein +interaction network from the STRING database, but it does so only to +produce an unweighted network that aggregates all types of interactions +not only the physical interaction subnetwork. Please refer to STRINGdb +website or R package to specify a PPI network suited for your +application.

Let’s take a look at the priors:

-# download motif and ppi file from AWS Bucket
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/motif_subset.txt")
-system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/ppi_subset.txt")
-motif <- read.delim("./motif_subset.txt", stringsAsFactors=F, header=F)
-ppi <- read.delim("./ppi_subset.txt", stringsAsFactors=F, header=F)
-ppi[1:5,]
-#>      V1      V2    V3
-#> 1 ASCL1   ASCL1 1.000
-#> 2 ASCL1 BHLHE41 0.314
-#> 3 ASCL1    DLX3 0.188
-#> 4 ASCL1     EN2 0.790
-#> 5 ASCL1    HES4 0.451
-motif[1:5,]
-#>      V1     V2 V3
-#> 1 ASCL1  RPAP3  0
-#> 2 ASCL1  ACSM3  0
-#> 3 ASCL1 CREBBP  0
-#> 4 ASCL1 ZNF263  0
-#> 5 ASCL1   RALA  0
-

Now we locate our expression data. As an example, we will use a subset of the GTEx version 7 RNA-Seq data, downloaded from https://gtexportal.org/home/datasets. We start with a subset of RNA-Seq data (tpm normalized) for 1,000 genes from 130 LCL cell line samples and 407 whole blood samples.

+# download motif and ppi file from AWS Bucket +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/motif_subset.txt") +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/ppi_subset.txt") +motif <- read.delim("./motif_subset.txt", stringsAsFactors=F, header=F) +ppi <- read.delim("./ppi_subset.txt", stringsAsFactors=F, header=F) +ppi[1:5,] +#> V1 V2 V3 +#> 1 ASCL1 ASCL1 1.000 +#> 2 ASCL1 BHLHE41 0.314 +#> 3 ASCL1 DLX3 0.188 +#> 4 ASCL1 EN2 0.790 +#> 5 ASCL1 HES4 0.451 +motif[1:5,] +#> V1 V2 V3 +#> 1 ASCL1 RPAP3 0 +#> 2 ASCL1 ACSM3 0 +#> 3 ASCL1 CREBBP 0 +#> 4 ASCL1 ZNF263 0 +#> 5 ASCL1 RALA 0
+

Now we locate our expression data. As an example, we will use a +subset of the GTEx version 7 RNA-Seq data, downloaded from https://gtexportal.org/home/datasets. We start with a +subset of RNA-Seq data (tpm normalized) for 1,000 genes from 130 LCL +cell line samples and 407 whole blood samples.

-# dowload and load the GTEx expression matrix (tpm normalized expression)
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/expression_tpm_lcl_blood_subset.txt")
-exp <- read.delim("./expression_tpm_lcl_blood_subset.txt", stringsAsFactors = F, check.names = F)
-# Log transform the tpm normalized expression
-exp <- log2(exp+1)
-# Determine the number of non-NA/non-zero rows in the expression data. This is to be able to ensure that PANDA will have enough values in the vectors to calculate pearson correlations between gene expression profiles in the construction of the gene co-expression prior.
-zero_na_counts <- apply(exp, MARGIN = 1, FUN = function(x) length(x[(!is.na(x) & x!=0) ]))
-# Maintain only genes with at least 20 valid gene expression entries
-exp <- exp[zero_na_counts > 20,]
-# # The set of genes in the expression, motif and ppi matrices must be the same
-# exp <- exp[rownames(exp) %in% motif$V2,]
-# motif_subset <- motif[(motif$V1 %in% rownames(exp)) & (motif$V2 %in% rownames(exp)),]
-# ppi_subset <- ppi[(ppi$V1 %in% motif_subset$V1) & (ppi$V2 %in% motif_subset$V1),]
-# Load the sample ids of LCL samples
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/LCL_samples.txt")
-lcl_samples <-read.delim("./LCL_samples.txt", header=FALSE, stringsAsFactors=FALSE)
-# Select the columns of the expression matrix corresponding to the LCL samples
-lcl_exp <- exp[,colnames(exp) %in% lcl_samples[,1]]
-# Load the sample ids of whole blood samples
-system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/WholeBlood_samples.txt")
-wblood_samples <-read.delim("./WholeBlood_samples.txt", header=FALSE, stringsAsFactors=FALSE)
-# Select the columns of the expression matrix corresponding to the whole blood samples
-wb_exp <- exp[,colnames(exp) %in% wblood_samples[,1]]
-

Now we run PANDA, pointing it to the parsed expression data, motif prior and ppi prior. We will point to the same motif and ppi priors for each PANDA run, which represents the initial putative regulatory information. We then point to the expression matrix correspoding to the LCL samples to generate the LCL regulatory network, and to the expression matrix corresponding to the whole blood samples to generate the whole blood regulatory network.

+# dowload and load the GTEx expression matrix (tpm normalized expression) +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/expression_tpm_lcl_blood_subset.txt") +exp <- read.delim("./expression_tpm_lcl_blood_subset.txt", stringsAsFactors = F, check.names = F) +# Log transform the tpm normalized expression +exp <- log2(exp+1) +# Determine the number of non-NA/non-zero rows in the expression data. This is to be able to ensure that PANDA will have enough values in the vectors to calculate pearson correlations between gene expression profiles in the construction of the gene co-expression prior. +zero_na_counts <- apply(exp, MARGIN = 1, FUN = function(x) length(x[(!is.na(x) & x!=0) ])) +# Maintain only genes with at least 20 valid gene expression entries +exp <- exp[zero_na_counts > 20,] +# # The set of genes in the expression, motif and ppi matrices must be the same +# exp <- exp[rownames(exp) %in% motif$V2,] +# motif_subset <- motif[(motif$V1 %in% rownames(exp)) & (motif$V2 %in% rownames(exp)),] +# ppi_subset <- ppi[(ppi$V1 %in% motif_subset$V1) & (ppi$V2 %in% motif_subset$V1),] +# Load the sample ids of LCL samples +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/LCL_samples.txt") +lcl_samples <-read.delim("./LCL_samples.txt", header=FALSE, stringsAsFactors=FALSE) +# Select the columns of the expression matrix corresponding to the LCL samples +lcl_exp <- exp[,colnames(exp) %in% lcl_samples[,1]] +# Load the sample ids of whole blood samples +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/WholeBlood_samples.txt") +wblood_samples <-read.delim("./WholeBlood_samples.txt", header=FALSE, stringsAsFactors=FALSE) +# Select the columns of the expression matrix corresponding to the whole blood samples +wb_exp <- exp[,colnames(exp) %in% wblood_samples[,1]]
+

Now we run PANDA, pointing it to the parsed expression data, motif +prior and ppi prior. We will point to the same motif and ppi priors for +each PANDA run, which represents the initial putative regulatory +information. We then point to the expression matrix correspoding to the +LCL samples to generate the LCL regulatory network, and to the +expression matrix corresponding to the whole blood samples to generate +the whole blood regulatory network.

-pandaLCL <- panda(motif, lcl_exp, ppi, mode="intersection")
-pandaWB  <- panda(motif, wb_exp, ppi, mode="intersection")
-pandaLCL
-#> PANDA network for968genes and50transcription factors.
-#> 
-#> Slots:
-#> regNet   : Regulatory network of 50 transcription factors to 968 genes.
-#> coregNet: Co-regulation network of 968 genes.
-#> coopNet  : Cooperative network of 50 transcription factors.
-#> Regulatory graph contains 48400 edges.
-#> Regulatory graph is complete.
-pandaWB
-#> PANDA network for968genes and50transcription factors.
-#> 
-#> Slots:
-#> regNet   : Regulatory network of 50 transcription factors to 968 genes.
-#> coregNet: Co-regulation network of 968 genes.
-#> coopNet  : Cooperative network of 50 transcription factors.
-#> Regulatory graph contains 48400 edges.
-#> Regulatory graph is complete.
-

The regulatory network (bipartite graph) with edge weights representing the “likelihood” that a transcription factor binds the promotor of and regulates the expression of a gene.

+pandaLCL <- panda(motif, lcl_exp, ppi, mode="intersection") +pandaWB <- panda(motif, wb_exp, ppi, mode="intersection") +pandaLCL +#> PANDA network for968genes and50transcription factors. +#> +#> Slots: +#> regNet : Regulatory network of 50 transcription factors to 968 genes. +#> coregNet: Co-regulation network of 968 genes. +#> coopNet : Cooperative network of 50 transcription factors. +#> Regulatory graph contains 48400 edges. +#> Regulatory graph is complete. +pandaWB +#> PANDA network for968genes and50transcription factors. +#> +#> Slots: +#> regNet : Regulatory network of 50 transcription factors to 968 genes. +#> coregNet: Co-regulation network of 968 genes. +#> coopNet : Cooperative network of 50 transcription factors. +#> Regulatory graph contains 48400 edges. +#> Regulatory graph is complete.
+

The regulatory network (bipartite graph) with edge weights +representing the “likelihood” that a transcription factor binds the +promotor of and regulates the expression of a gene.

-# The bipartite regulatory network (transcription factors as rows and target genes as columns)
-regNetLCL <- pandaLCL@regNet
-regNetLCL[1:5,1:5]
-#>         RP4-669L17.8    MTND2P28        HES4     ACTRT2 RP3-467L1.6
-#> ASCL1     -0.4338663 -0.06046052  0.43546720 -0.3541327  -0.1055712
-#> BARX1     -0.5516320 -0.83640043 -0.68106570 -0.2683416   0.0649070
-#> BHLHE41   -0.4478896 -0.22003403 -0.03953981 -0.2154970  -0.4435292
-#> CEBPD     -0.1053101  0.06674693 -0.04042170 -0.2018673  -0.5209635
-#> CEBPZ     -0.2151449 -0.16801549 -0.12427431 -0.3099354  -0.5307553
-regNetWB <- pandaWB@regNet
+# The bipartite regulatory network (transcription factors as rows and target genes as columns) +regNetLCL <- pandaLCL@regNet +regNetLCL[1:5,1:5] +#> RP4-669L17.8 MTND2P28 HES4 ACTRT2 RP3-467L1.6 +#> ASCL1 -0.4338663 -0.06046052 0.43546720 -0.3541327 -0.1055712 +#> BARX1 -0.5516320 -0.83640043 -0.68106570 -0.2683416 0.0649070 +#> BHLHE41 -0.4478896 -0.22003403 -0.03953981 -0.2154970 -0.4435292 +#> CEBPD -0.1053101 0.06674693 -0.04042170 -0.2018673 -0.5209635 +#> CEBPZ -0.2151449 -0.16801549 -0.12427431 -0.3099354 -0.5307553 +regNetWB <- pandaWB@regNet
-
-

-Visualizing networks in Cytoscape

-

In this section we will visualize parts of the network using the Cytoscape software. Download Cytoscape from: https://cytoscape.org and have the software open before calling the function.

-
-

-Preparing data to plot

+
+

Visualizing networks in Cytoscape +

+

In this section we will visualize parts of the network using the +Cytoscape software. Download Cytoscape from: https://cytoscape.org and +have the software open before calling the function.

+
+

Preparing data to plot +

-# We will use the function vis.panda.in.cytoscape to plot a set of nodes and edges on Cytoscape. The input for this function is a data.frame of edges to plot with 4 columns: "tf", "gene", "motif" (TF motif present or not on gene promoter), "force" (edge weight calculated by PANDA).
-lcl_vis <- reshape2::melt(pandaLCL@regNet)
-wb_vis  <- reshape2::melt(pandaWB@regNet)
-lcl_vis <- data.frame("TF"=as.character(lcl_vis[,1]),"Gene"=as.character(lcl_vis[,2]),"Motif"=NA,"Score"=as.numeric(lcl_vis[,3]),stringsAsFactors = FALSE)
-wb_vis <- data.frame("TF"=as.character(wb_vis[,1]),"Gene"=as.character(wb_vis[,2]),"Motif"=NA,"Score"=as.numeric(wb_vis[,3]),stringsAsFactors = FALSE)
-head(lcl_vis)
-#>        TF         Gene Motif      Score
-#> 1   ASCL1 RP4-669L17.8    NA -0.4338663
-#> 2   BARX1 RP4-669L17.8    NA -0.5516320
-#> 3 BHLHE41 RP4-669L17.8    NA -0.4478896
-#> 4   CEBPD RP4-669L17.8    NA -0.1053101
-#> 5   CEBPZ RP4-669L17.8    NA -0.2151449
-#> 6   CENPB RP4-669L17.8    NA -0.1024927
+# We will use the function vis.panda.in.cytoscape to plot a set of nodes and edges on Cytoscape. The input for this function is a data.frame of edges to plot with 4 columns: "tf", "gene", "motif" (TF motif present or not on gene promoter), "force" (edge weight calculated by PANDA). +lcl_vis <- reshape2::melt(pandaLCL@regNet) +wb_vis <- reshape2::melt(pandaWB@regNet) +lcl_vis <- data.frame("TF"=as.character(lcl_vis[,1]),"Gene"=as.character(lcl_vis[,2]),"Motif"=NA,"Score"=as.numeric(lcl_vis[,3]),stringsAsFactors = FALSE) +wb_vis <- data.frame("TF"=as.character(wb_vis[,1]),"Gene"=as.character(wb_vis[,2]),"Motif"=NA,"Score"=as.numeric(wb_vis[,3]),stringsAsFactors = FALSE) +head(lcl_vis) +#> TF Gene Motif Score +#> 1 ASCL1 RP4-669L17.8 NA -0.4338663 +#> 2 BARX1 RP4-669L17.8 NA -0.5516320 +#> 3 BHLHE41 RP4-669L17.8 NA -0.4478896 +#> 4 CEBPD RP4-669L17.8 NA -0.1053101 +#> 5 CEBPZ RP4-669L17.8 NA -0.2151449 +#> 6 CENPB RP4-669L17.8 NA -0.1024927
-
-

-Plot the 200 highest edge weights

+
+

Plot the 200 highest edge weights +

-n=200 # number of edges to plot 
-top <- order(lcl_vis$Score,decreasing=T)[1:n]
-lcl_vis_top <- lcl_vis[top,]
-# Plot in cytoscape (open Cytoscape before running this command)
-visPandaInCytoscape(lcl_vis_top, network_name="LCL")
-# Here we will load a customized visual style for our network, in which TF nodes are orange circles, target gene nodes are blue squares, and edges shade and width are the edge weight (likelyhood of regulatory interaction between the TF and gene). You can further customize the network style directly from Cytoscape.
-createPandaStyle(style_name="PandaStyle")
+n=200 # number of edges to plot +top <- order(lcl_vis$Score,decreasing=T)[1:n] +lcl_vis_top <- lcl_vis[top,] +# Plot in cytoscape (open Cytoscape before running this command) +visPandaInCytoscape(lcl_vis_top, network_name="LCL") +# Here we will load a customized visual style for our network, in which TF nodes are orange circles, target gene nodes are blue squares, and edges shade and width are the edge weight (likelyhood of regulatory interaction between the TF and gene). You can further customize the network style directly from Cytoscape. +createPandaStyle(style_name="PandaStyle")
-
-

-Plot the top differential edges betwen LCL and WB

+
+

Plot the top differential edges betwen LCL and WB +

-# Select the top differential edge weights betweeen LCL and whole blood
-diffRes <- pandaDiffEdges(lcl_vis, wb_vis, condition_name="LCL")
-head(diffRes)
-# Number of differential edges is:
-nrow(diffRes)
-# Select the top differential edges higher in LCL to plot in Cytoscape
-n=200 # number of edges to select from each condition
-diffResLCL <- diffRes[diffRes$LCL=="T",]
-diffResLCL <- diffResLCL[order(diffResLCL$Score,decreasing=TRUE),][1:n,]
-# Select the top differential edges higher in whole blood to plot in Cytoscape
-diffResWB <- diffRes[diffRes$LCL=="F",]
-diffResWB <- diffResWB[order(diffResWB$Score,decreasing=TRUE),][1:n,]
-# Combine top differential edges in LCL and WB to plot in Cytoscape
-diffRes_vis <- rbind(diffResLCL, diffResWB)
-# Plot the network (open Cytoscape before running this command)
-# Purple edges indicate higher edge weight in the defined "condition_name" parameter (LCL in our example), and green edges indicate higher edge weight in the other condition (whole blood in our example).
-visDiffPandaInCytoscape(diffRes_vis, condition_name = "LCL", network_name="diff.PANDA")
-# Apply the style to the network
-createDiffPandaStyle(style_name="Diff.PandaStyle", condition_name="LCL")
+# Select the top differential edge weights betweeen LCL and whole blood +diffRes <- pandaDiffEdges(lcl_vis, wb_vis, condition_name="LCL") +head(diffRes) +# Number of differential edges is: +nrow(diffRes) +# Select the top differential edges higher in LCL to plot in Cytoscape +n=200 # number of edges to select from each condition +diffResLCL <- diffRes[diffRes$LCL=="T",] +diffResLCL <- diffResLCL[order(diffResLCL$Score,decreasing=TRUE),][1:n,] +# Select the top differential edges higher in whole blood to plot in Cytoscape +diffResWB <- diffRes[diffRes$LCL=="F",] +diffResWB <- diffResWB[order(diffResWB$Score,decreasing=TRUE),][1:n,] +# Combine top differential edges in LCL and WB to plot in Cytoscape +diffRes_vis <- rbind(diffResLCL, diffResWB) +# Plot the network (open Cytoscape before running this command) +# Purple edges indicate higher edge weight in the defined "condition_name" parameter (LCL in our example), and green edges indicate higher edge weight in the other condition (whole blood in our example). +visDiffPandaInCytoscape(diffRes_vis, condition_name = "LCL", network_name="diff.PANDA") +# Apply the style to the network +createDiffPandaStyle(style_name="Diff.PandaStyle", condition_name="LCL")
-
-

-Calculating degree

+
+

Calculating degree +

    -
  • out-degrees of TFs: sum of the weights of outbound edges around a TF
    • -
  • in-degrees of genes: sum of the weights of inbound edges around a gene
    • +
  • out-degrees of TFs: sum of the weights of outbound edges around a +TF
    • +
  • in-degrees of genes: sum of the weights of inbound edges around a +gene
-lcl_outdegree <- calcDegree(pandaLCL, type="tf")
-wb_outdegree  <- calcDegree(pandaWB, type="tf")
-lcl_indegree  <- calcDegree(pandaLCL, type="gene")
-wb_indegree   <- calcDegree(pandaWB, type="gene")
-# Calculate the gene in-degree difference for two different panda regulatory networks (LCL minus whole blood)
-degreeDiff <- calcDegreeDifference(pandaLCL, pandaWB, type="gene")
-head(degreeDiff)
-#> RP4-669L17.8     MTND2P28         HES4       ACTRT2  RP3-467L1.6        FBXO6 
-#>   -0.4731904   -4.5354668   -0.7550223    0.6174926    0.2393536   -0.5087543
+lcl_outdegree <- calcDegree(pandaLCL, type="tf") +wb_outdegree <- calcDegree(pandaWB, type="tf") +lcl_indegree <- calcDegree(pandaLCL, type="gene") +wb_indegree <- calcDegree(pandaWB, type="gene") +# Calculate the gene in-degree difference for two different panda regulatory networks (LCL minus whole blood) +degreeDiff <- calcDegreeDifference(pandaLCL, pandaWB, type="gene") +head(degreeDiff) +#> RP4-669L17.8 MTND2P28 HES4 ACTRT2 RP3-467L1.6 FBXO6 +#> -0.4731904 -4.5354668 -0.7550223 0.6174926 0.2393536 -0.5087543
-
-

-Gene Set Enrichment Analysis

-

Well will use the fgsea package to perform gene set enrichment analysis. We need to point to a ranked gene list (for example the gene in-degree difference between LCL and whole blood), and a list of gene sets (or signatures) in gmt format to test for enrichment. The gene sets can be downloaded from MSigDB: http://software.broadinstitute.org/gsea/msigdb Same gene annotation should be used in the ranked gene list and gene sets. In our example we will use the KEGG pathways downloaded from MSigDB.

-
-

-Run fgsea

+
+

Gene Set Enrichment Analysis +

+

Well will use the fgsea package to perform gene set enrichment +analysis. We need to point to a ranked gene list (for example the gene +in-degree difference between LCL and whole blood), and a list of gene +sets (or signatures) in gmt format to test for enrichment. The gene sets +can be downloaded from MSigDB: http://software.broadinstitute.org/gsea/msigdb Same gene +annotation should be used in the ranked gene list and gene sets. In our +example we will use the KEGG pathways downloaded from MSigDB.

+
+

Run fgsea +

-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/c2.cp.kegg.v7.0.symbols.gmt")
-pathways <- gmtPathways("./c2.cp.kegg.v7.0.symbols.gmt")
-# To retrieve biological-relevant processes, we will load and use the complete ranked gene list (27,175 genes) calculated from the complete network instead of the 1,000 subset genes we used in this tutorial example to build PANDA networks within a very short run time.
-system("curl -O  https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/lclWB_indegreeDifference.rnk")
-degreeDiff_all <- read.delim("./lclWB_indegreeDifference.rnk",stringsAsFactors = F,header=F)
-degreeDiff_all <- setNames(degreeDiff_all[,2], degreeDiff_all[,1])
-fgseaRes <- fgsea(pathways, degreeDiff_all, minSize=15, maxSize=500, nperm=1000)
-head(fgseaRes)
-#>                                          pathway        pval       padj
-#> 1:               KEGG_GLYCOLYSIS_GLUCONEOGENESIS 0.029850746 0.08991319
-#> 2:                  KEGG_CITRATE_CYCLE_TCA_CYCLE 0.001831502 0.01749271
-#> 3:                KEGG_PENTOSE_PHOSPHATE_PATHWAY 0.083885210 0.17792202
-#> 4: KEGG_PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS 0.648230088 0.71807684
-#> 5:          KEGG_FRUCTOSE_AND_MANNOSE_METABOLISM 0.037444934 0.10402654
-#> 6:                     KEGG_GALACTOSE_METABOLISM 0.025641026 0.08391204
-#>            ES        NES nMoreExtreme size
-#> 1:  0.3355458  1.4197869           11   58
-#> 2: -0.5923935 -2.0056899            0   29
-#> 3:  0.4235984  1.3970008           37   21
-#> 4:  0.2675875  0.8884628          292   22
-#> 5:  0.4168076  1.5152490           16   30
-#> 6:  0.4491757  1.5737014           11   25
-#>                                        leadingEdge
-#> 1:         ALDH3B1,LDHAL6A,PGM1,PKM,ALDOA,ENO1,...
-#> 2:              MDH2,CS,DLST,SDHD,IDH3B,SUCLG2,...
-#> 3:               H6PD,PGM1,ALDOA,PFKM,PFKL,RPE,...
-#> 4: UGT1A10,UGT1A1,UGT2B15,UGT1A6,UGT2B17,CRYL1,...
-#> 5:              PFKFB4,ALDOA,HK3,PFKM,HK1,PFKL,...
-#> 6:                  GAA,PGM1,HK3,GLB1,PFKM,HK1,...
-# Subset to pathways with FDR < 0.05
-sig <- fgseaRes[fgseaRes$padj < 0.05,]
-# Get the top 10 significant pathways enriched for genes having lower targeting in LCLs  
-sig[order(sig$NES)[1:10],]
-#>                              pathway        pval       padj         ES
-#>  1:                 KEGG_SPLICEOSOME 0.001647446 0.01749271 -0.6155980
-#>  2:                    KEGG_RIBOSOME 0.001663894 0.01749271 -0.6438056
-#>  3:             KEGG_DNA_REPLICATION 0.001798561 0.01749271 -0.7301502
-#>  4:                  KEGG_CELL_CYCLE 0.001626016 0.01749271 -0.5352336
-#>  5:  KEGG_NUCLEOTIDE_EXCISION_REPAIR 0.001712329 0.01749271 -0.5896497
-#>  6:              KEGG_PROTEIN_EXPORT 0.001818182 0.01749271 -0.6735362
-#>  7: KEGG_AMINOACYL_TRNA_BIOSYNTHESIS 0.001733102 0.01749271 -0.5489421
-#>  8:     KEGG_CITRATE_CYCLE_TCA_CYCLE 0.001831502 0.01749271 -0.5923935
-#>  9: KEGG_BASAL_TRANSCRIPTION_FACTORS 0.001795332 0.01749271 -0.5641948
-#> 10:             KEGG_MISMATCH_REPAIR 0.001818182 0.01749271 -0.5977217
-#>           NES nMoreExtreme size                                   leadingEdge
-#>  1: -2.762220            0  116 SRSF6,SRSF3,SRSF7,HNRNPA1L2,PRPF38A,PPIL1,...
-#>  2: -2.721909            0   80      RPS2,RPL23A,RPL19,RPS12,RPS17,RPL10A,...
-#>  3: -2.594473            0   34           RFC3,RFC5,RPA2,POLE3,PRIM1,PCNA,...
-#>  4: -2.422663            0  121         YWHAQ,ORC6,DBF4,ANAPC10,BUB3,WEE1,...
-#>  5: -2.212853            0   41            XPA,RFC3,RFC5,RPA2,POLE3,ERCC8,...
-#>  6: -2.146583            0   22      SRP9,IMMP1L,HSPA5,SEC11C,OXA1L,SPCS2,...
-#>  7: -2.036029            0   40         MARS2,NARS,YARS,FARS2,YARS2,NARS2,...
-#>  8: -2.005690            0   29            MDH2,CS,DLST,SDHD,IDH3B,SUCLG2,...
-#>  9: -1.965811            0   31          TAF4,TAF7,TAF9,GTF2E1,GTF2F2,TBP,...
-#> 10: -1.904960            0   22             RFC3,RFC5,RPA2,PCNA,MSH2,RPA1,...
+system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/c2.cp.kegg.v7.0.symbols.gmt") +pathways <- gmtPathways("./c2.cp.kegg.v7.0.symbols.gmt") +# To retrieve biological-relevant processes, we will load and use the complete ranked gene list (27,175 genes) calculated from the complete network instead of the 1,000 subset genes we used in this tutorial example to build PANDA networks within a very short run time. +system("curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooR/tutorial_datasets/lclWB_indegreeDifference.rnk") +degreeDiff_all <- read.delim("./lclWB_indegreeDifference.rnk",stringsAsFactors = F,header=F) +degreeDiff_all <- setNames(degreeDiff_all[,2], degreeDiff_all[,1]) +fgseaRes <- fgsea(pathways, degreeDiff_all, minSize=15, maxSize=500, nperm=1000) +head(fgseaRes) +#> pathway pval padj +#> 1: KEGG_GLYCOLYSIS_GLUCONEOGENESIS 0.028103044 0.08655794 +#> 2: KEGG_CITRATE_CYCLE_TCA_CYCLE 0.001821494 0.02039142 +#> 3: KEGG_PENTOSE_PHOSPHATE_PATHWAY 0.114893617 0.24086132 +#> 4: KEGG_PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS 0.640591966 0.74308668 +#> 5: KEGG_FRUCTOSE_AND_MANNOSE_METABOLISM 0.037946429 0.10649482 +#> 6: KEGG_GALACTOSE_METABOLISM 0.033333333 0.09508197 +#> ES NES nMoreExtreme size +#> 1: 0.3355458 1.3734374 11 58 +#> 2: -0.5923935 -2.0112091 0 29 +#> 3: 0.4235984 1.3633474 53 21 +#> 4: 0.2675875 0.8728426 302 22 +#> 5: 0.4168076 1.4768085 16 30 +#> 6: 0.4491757 1.5270319 14 25 +#> leadingEdge +#> 1: ALDH3B1,LDHAL6A,PGM1,PKM,ALDOA,ENO1,... +#> 2: MDH2,CS,DLST,SDHD,IDH3B,SUCLG2,... +#> 3: H6PD,PGM1,ALDOA,PFKM,PFKL,RPE,... +#> 4: UGT1A10,UGT1A1,UGT2B15,UGT1A6,UGT2B17,CRYL1,... +#> 5: PFKFB4,ALDOA,HK3,PFKM,HK1,PFKL,... +#> 6: GAA,PGM1,HK3,GLB1,PFKM,HK1,... +# Subset to pathways with FDR < 0.05 +sig <- fgseaRes[fgseaRes$padj < 0.05,] +# Get the top 10 significant pathways enriched for genes having lower targeting in LCLs +sig[order(sig$NES)[1:10],] +#> pathway pval padj ES +#> 1: KEGG_SPLICEOSOME 0.001666667 0.02039142 -0.6155980 +#> 2: KEGG_RIBOSOME 0.001694915 0.02039142 -0.6438056 +#> 3: KEGG_DNA_REPLICATION 0.001788909 0.02039142 -0.7301502 +#> 4: KEGG_CELL_CYCLE 0.001692047 0.02039142 -0.5352336 +#> 5: KEGG_NUCLEOTIDE_EXCISION_REPAIR 0.001792115 0.02039142 -0.5896497 +#> 6: KEGG_PROTEIN_EXPORT 0.001890359 0.02039142 -0.6735362 +#> 7: KEGG_CITRATE_CYCLE_TCA_CYCLE 0.001821494 0.02039142 -0.5923935 +#> 8: KEGG_AMINOACYL_TRNA_BIOSYNTHESIS 0.001801802 0.02039142 -0.5489421 +#> 9: KEGG_BASAL_TRANSCRIPTION_FACTORS 0.001792115 0.02039142 -0.5641948 +#> 10: KEGG_MISMATCH_REPAIR 0.001890359 0.02039142 -0.5977217 +#> NES nMoreExtreme size leadingEdge +#> 1: -2.736120 0 116 SRSF6,SRSF3,SRSF7,HNRNPA1L2,PRPF38A,PPIL1,... +#> 2: -2.711268 0 80 RPS2,RPL23A,RPL19,RPS12,RPS17,RPL10A,... +#> 3: -2.566480 0 34 RFC3,RFC5,RPA2,POLE3,PRIM1,PCNA,... +#> 4: -2.381209 0 121 YWHAQ,ORC6,DBF4,ANAPC10,BUB3,WEE1,... +#> 5: -2.175045 0 41 XPA,RFC3,RFC5,RPA2,POLE3,ERCC8,... +#> 6: -2.108399 0 22 SRP9,IMMP1L,HSPA5,SEC11C,OXA1L,SPCS2,... +#> 7: -2.011209 0 29 MDH2,CS,DLST,SDHD,IDH3B,SUCLG2,... +#> 8: -2.005774 0 40 MARS2,NARS,YARS,FARS2,YARS2,NARS2,... +#> 9: -1.953798 0 31 TAF4,TAF7,TAF9,GTF2E1,GTF2F2,TBP,... +#> 10: -1.871073 0 22 RFC3,RFC5,RPA2,PCNA,MSH2,RPA1,...
-
-

-Bubble plot of top differentially targeted pathways

-

Bubble plot of gene sets (KEGG pathways) on y-axis and adjusted p-value (padj) on x-axis. Bubble size indicates the number of genes in each gene set, and bubble color indicates the normalized enrichment score (NES). Blue is for negative NES (enrichment of higher targeted genes in whole blood), and red is for positive NES (enrichment of higher targeted genes in LCL).

+
+

Bubble plot of top differentially targeted pathways +

+

Bubble plot of gene sets (KEGG pathways) on y-axis and adjusted +p-value (padj) on x-axis. Bubble size indicates the number of genes in +each gene set, and bubble color indicates the normalized enrichment +score (NES). Blue is for negative NES (enrichment of higher targeted +genes in whole blood), and red is for positive NES (enrichment of higher +targeted genes in LCL).

-dat <- data.frame(fgseaRes)
-# Settings
-fdrcut <- 0.05 # FDR cut-off to use as output for significant signatures
-dencol_neg <- "blue" # bubble plot color for negative ES
-dencol_pos <- "red" # bubble plot color for positive ES
-signnamelength <- 4 # set to remove prefix from signature names (2 for "GO", 4 for "KEGG", 8 for "REACTOME")
-asp <- 3 # aspect ratio of bubble plot
-charcut <- 100 # cut signature name in heatmap to this nr of characters
-# Make signature names more readable
-a <- as.character(dat$pathway) # 'a' is a great variable name to substitute row names with something more readable
-for (j in 1:length(a)){
-  a[j] <- substr(a[j], signnamelength+2, nchar(a[j]))
-}
-a <- tolower(a) # convert to lower case (you may want to comment this out, it really depends on what signatures you are looking at, c6 signatures contain gene names, and converting those to lower case may be confusing)
-for (j in 1:length(a)){
-  if(nchar(a[j])>charcut) { a[j] <- paste(substr(a[j], 1, charcut), "...", sep=" ")}
-} # cut signature names that have more characters than charcut, and add "..."
-a <- gsub("_", " ", a)
-dat$NAME <- a
-# Determine what signatures to plot (based on FDR cut)
-dat2 <- dat[dat[,"padj"]<fdrcut,]
-dat2 <- dat2[order(dat2[,"padj"]),] 
-dat2$signature <- factor(dat2$NAME, rev(as.character(dat2$NAME)))
-# Determine what labels to color
-sign_neg <- which(dat2[,"NES"]<0)
-sign_pos <- which(dat2[,"NES"]>0)
-# Color labels
-signcol <- rep(NA, length(dat2$signature))
-signcol[sign_neg] <- dencol_neg # text color of negative signatures
-signcol[sign_pos] <- dencol_pos # text color of positive signatures
-signcol <- rev(signcol) # need to revert vector of colors, because ggplot starts plotting these from below
-# Plot bubble plot
-g<-ggplot(dat2, aes(x=padj,y=signature,size=size))
-g+geom_point(aes(fill=NES), shape=21, colour="white")+
-  theme_bw()+ # white background, needs to be placed before the "signcol" line
-  xlim(0,fdrcut)+
-  scale_size_area(max_size=10,guide="none")+
-  scale_fill_gradient2(low=dencol_neg, high=dencol_pos)+
-  theme(axis.text.y = element_text(colour=signcol))+
-  theme(aspect.ratio=asp, axis.title.y=element_blank()) # test aspect.ratio
+dat <- data.frame(fgseaRes) +# Settings +fdrcut <- 0.05 # FDR cut-off to use as output for significant signatures +dencol_neg <- "blue" # bubble plot color for negative ES +dencol_pos <- "red" # bubble plot color for positive ES +signnamelength <- 4 # set to remove prefix from signature names (2 for "GO", 4 for "KEGG", 8 for "REACTOME") +asp <- 3 # aspect ratio of bubble plot +charcut <- 100 # cut signature name in heatmap to this nr of characters +# Make signature names more readable +a <- as.character(dat$pathway) # 'a' is a great variable name to substitute row names with something more readable +for (j in 1:length(a)){ + a[j] <- substr(a[j], signnamelength+2, nchar(a[j])) +} +a <- tolower(a) # convert to lower case (you may want to comment this out, it really depends on what signatures you are looking at, c6 signatures contain gene names, and converting those to lower case may be confusing) +for (j in 1:length(a)){ + if(nchar(a[j])>charcut) { a[j] <- paste(substr(a[j], 1, charcut), "...", sep=" ")} +} # cut signature names that have more characters than charcut, and add "..." +a <- gsub("_", " ", a) +dat$NAME <- a +# Determine what signatures to plot (based on FDR cut) +dat2 <- dat[dat[,"padj"]<fdrcut,] +dat2 <- dat2[order(dat2[,"padj"]),] +dat2$signature <- factor(dat2$NAME, rev(as.character(dat2$NAME))) +# Determine what labels to color +sign_neg <- which(dat2[,"NES"]<0) +sign_pos <- which(dat2[,"NES"]>0) +# Color labels +signcol <- rep(NA, length(dat2$signature)) +signcol[sign_neg] <- dencol_neg # text color of negative signatures +signcol[sign_pos] <- dencol_pos # text color of positive signatures +signcol <- rev(signcol) # need to revert vector of colors, because ggplot starts plotting these from below +# Plot bubble plot +g<-ggplot(dat2, aes(x=padj,y=signature,size=size)) +g+geom_point(aes(fill=NES), shape=21, colour="white")+ + theme_bw()+ # white background, needs to be placed before the "signcol" line + xlim(0,fdrcut)+ + scale_size_area(max_size=10,guide="none")+ + scale_fill_gradient2(low=dencol_neg, high=dencol_pos)+ + theme(axis.text.y = element_text(colour=signcol))+ + theme(aspect.ratio=asp, axis.title.y=element_blank()) # test aspect.ratio

-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/pandaRApplicationinGTExData_files/figure-html/unnamed-chunk-14-1.png b/docs/articles/pandaRApplicationinGTExData_files/figure-html/unnamed-chunk-14-1.png index 7feac827..e698c41c 100644 Binary files a/docs/articles/pandaRApplicationinGTExData_files/figure-html/unnamed-chunk-14-1.png and b/docs/articles/pandaRApplicationinGTExData_files/figure-html/unnamed-chunk-14-1.png differ diff --git a/docs/articles/pandaR_files/figure-html/plotZ-1.png b/docs/articles/pandaR_files/figure-html/plotZ-1.png index 8d470c35..811489b5 100644 Binary files a/docs/articles/pandaR_files/figure-html/plotZ-1.png and b/docs/articles/pandaR_files/figure-html/plotZ-1.png differ diff --git a/docs/articles/pandaR_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/pandaR_files/figure-html/unnamed-chunk-6-1.png index 8a3aa393..0a8580e8 100644 Binary files a/docs/articles/pandaR_files/figure-html/unnamed-chunk-6-1.png and b/docs/articles/pandaR_files/figure-html/unnamed-chunk-6-1.png differ diff --git a/docs/articles/yarn.html b/docs/articles/yarn.html index 6aba91f8..a8867112 100644 --- a/docs/articles/yarn.html +++ b/docs/articles/yarn.html @@ -4,400 +4,386 @@ - + + YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization • netZooR - - - + + + - - + - -
-
-
-
-

YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization

-

Joseph N. Paulson & John Quackenbush

+ + +
+
+

YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization

+

Joseph N. +Paulson & John Quackenbush

-

2022-08-16

+

2023-09-22

- Source: vignettes/yarn.Rmd - - + Source: vignettes/yarn.Rmd +
yarn.Rmd
-
-

-YARN - Yet Another RNa-seq package

-

The goal of yarn is to expedite large RNA-seq analyses using a combination of previously developed tools. Yarn is meant to make it easier for the user to perform accurate comparison of conditions by leveraging many Bioconductor tools and various statistical and normalization techniques while accounting for the large heterogeneity and sparsity found in very large RNA-seq experiments.

+
+

YARN - Yet Another RNa-seq package +

+

The goal of yarn is to expedite large RNA-seq analyses using a +combination of previously developed tools. Yarn is meant to make it +easier for the user to perform accurate comparison of conditions by +leveraging many Bioconductor tools and various statistical and +normalization techniques while accounting for the large heterogeneity +and sparsity found in very large RNA-seq experiments.

-
-

-Installation

+
+

Installation +

You can install yarn from github through netZooR with:

-if (!requireNamespace("BiocManager", quietly=TRUE))
-    install.packages("BiocManager")
-BiocManager::install("netZooR")
+if (!requireNamespace("BiocManager", quietly=TRUE)) + install.packages("BiocManager") +BiocManager::install("netZooR")
-
-

-Quick Introduction

-

If you’re here to grab the GTEx version 6.0 data then look no further than this gist that uses yarn to download all the data and preprocess it for you.

+
+

Quick Introduction +

+

If you’re here to grab the GTEx version 6.0 data then look no further +than this gist +that uses yarn to download all the data and preprocess it for you.

-
-

-Preprocessing

-

Below are a few of the functions we can use to preprocess a large RNA-seq experiment. We follow a particular procedure where we:

+
+

Preprocessing +

+

Below are a few of the functions we can use to preprocess a large +RNA-seq experiment. We follow a particular procedure where we:

  1. Filter poor quality samples
  2. Merge samples of similar conditions for increased power
  3. Filter genes while preserving tissue or group specificity
    4. -
  4. Normalize while accounting for global differences in tissue distribution
    5. +
  5. Normalize while accounting for global differences in tissue +distribution
-

We will make use of the skin dataset for examples. The skin dataset is a small sample of the full GTEx data that can be downloaded using the downloadGTEx function. The skin dataset looks like this:

+

We will make use of the skin dataset for examples. The +skin dataset is a small sample of the full GTEx data that +can be downloaded using the downloadGTEx function. The +skin dataset looks like this:

-skin
-
## ExpressionSet (storageMode: lockedEnvironment)
-## assayData: 40824 features, 20 samples 
-##   element names: exprs 
-## protocolData: none
-## phenoData
-##   rowNames: GTEX-OHPL-0008-SM-4E3I9 GTEX-145MN-1526-SM-5SI9T ...
-##     GTEX-144FL-0626-SM-5LU43 (20 total)
-##   varLabels: SAMPID SMATSSCR ... DTHHRDY (65 total)
-##   varMetadata: labelDescription
-## featureData
-##   featureNames: 48350 48365 ... 7565 (40824 total)
-##   fvarLabels: ensembl_gene_id hgnc_symbol ... gene_biotype (6 total)
-##   fvarMetadata: labelDescription
-## experimentData: use 'experimentData(object)'
-## Annotation:
+skin
+
## ExpressionSet (storageMode: lockedEnvironment)
+## assayData: 40824 features, 20 samples 
+##   element names: exprs 
+## protocolData: none
+## phenoData
+##   rowNames: GTEX-OHPL-0008-SM-4E3I9 GTEX-145MN-1526-SM-5SI9T ...
+##     GTEX-144FL-0626-SM-5LU43 (20 total)
+##   varLabels: SAMPID SMATSSCR ... DTHHRDY (65 total)
+##   varMetadata: labelDescription
+## featureData
+##   featureNames: 48350 48365 ... 7565 (40824 total)
+##   fvarLabels: ensembl_gene_id hgnc_symbol ... gene_biotype (6 total)
+##   fvarMetadata: labelDescription
+## experimentData: use 'experimentData(object)'
+## Annotation:

This is a basic workflow. Details will be fleshed out:

  1. First always remember to have the library loaded.
+library(netZooR)
    -
  1. Download the GTEx gene count data as an ExpressionSet object or load the sample skin dataset.
    2. +
  2. Download the GTEx gene count data as an ExpressionSet object or load +the sample skin dataset.
-

For computational reasons we load the sample skin data instead of having the user download the

+

For computational reasons we load the sample skin data instead of +having the user download the

-library(netZooR)
-load("skin.rdata")
+library(netZooR) +load("skin.rdata")
    -
  1. Check mis-annotation of gender or other phenotypes using group-specific genes
    2. +
  2. Check mis-annotation of gender or other phenotypes using +group-specific genes
-yarn::checkMisAnnotation(skin,"GENDER",controlGenes="Y",legendPosition="topleft")
+yarn::checkMisAnnotation(skin,"GENDER",controlGenes="Y",legendPosition="topleft")

  1. Decide what sub-groups should be merged
-yarn::checkTissuesToMerge(skin,"SMTS","SMTSD")
+yarn::checkTissuesToMerge(skin,"SMTS","SMTSD")

  1. Filter lowly expressed genes
-skin_filtered = yarn::filterLowGenes(skin,"SMTSD")
-dim(skin)
-
## Features  Samples 
-##    40824       20
+skin_filtered = yarn::filterLowGenes(skin,"SMTSD") +dim(skin)
+
## Features  Samples 
+##    40824       20
-dim(skin_filtered)
-
## Features  Samples 
-##    19933       20
+dim(skin_filtered)
+
## Features  Samples 
+##    19933       20

Or group specific genes

-tmp = yarn::filterGenes(skin,labels=c("X","Y","MT"),featureName = "chromosome_name")
-# Keep only the sex names
-tmp = yarn::filterGenes(skin,labels=c("X","Y","MT"),featureName = "chromosome_name",keepOnly=TRUE)
+tmp = yarn::filterGenes(skin,labels=c("X","Y","MT"),featureName = "chromosome_name") +# Keep only the sex names +tmp = yarn::filterGenes(skin,labels=c("X","Y","MT"),featureName = "chromosome_name",keepOnly=TRUE)
  1. Normalize in a tissue or group-aware manner
-yarn::plotDensity(skin_filtered,"SMTSD",main=expression('log'[2]*' raw expression'))
+yarn::plotDensity(skin_filtered,"SMTSD",main=expression('log'[2]*' raw expression'))

-skin_filtered = yarn::normalizeTissueAware(skin_filtered,"SMTSD")
-yarn::plotDensity(skin_filtered,"SMTSD",normalized=TRUE,main="Normalized")
-
## normalizedMatrix is assumed to already be log-transformed
+skin_filtered = yarn::normalizeTissueAware(skin_filtered,"SMTSD") +yarn::plotDensity(skin_filtered,"SMTSD",normalized=TRUE,main="Normalized")
+
## normalizedMatrix is assumed to already be log-transformed

-
-

-Helper functions

-

Other than checkMisAnnotation and checkTissuesToMerge we provide a few plotting function. We include, plotCMDS, plotDensity, plotHeatmap.

-

plotCMDS - PCoA / Classical Multi-Dimensional Scaling of the most variable genes.

+
+

Helper functions +

+

Other than checkMisAnnotation and +checkTissuesToMerge we provide a few plotting function. We +include, plotCMDS, plotDensity, +plotHeatmap.

+

plotCMDS - PCoA / Classical Multi-Dimensional Scaling of +the most variable genes.

-data(skin)
-res = yarn::plotCMDS(skin,pch=21,bg=factor(pData(skin)$SMTSD))
+data(skin) +res = yarn::plotCMDS(skin,pch=21,bg=factor(pData(skin)$SMTSD))

-

plotDensity - Density plots colored by phenotype of choosing. Allows for inspection of global trend differences.

+

plotDensity - Density plots colored by phenotype of +choosing. Allows for inspection of global trend differences.

-filtData = yarn::filterLowGenes(skin,"SMTSD")
-yarn::plotDensity(filtData,groups="SMTSD",legendPos="topleft")
+filtData = yarn::filterLowGenes(skin,"SMTSD") +yarn::plotDensity(filtData,groups="SMTSD",legendPos="topleft")

plotHeatmap - Heatmap of the most variable genes.

-library(RColorBrewer)
-tissues = pData(skin)$SMTSD
-heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(tissues))]
-heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50)
-yarn::plotHeatmap(skin,normalized=FALSE,log=TRUE,trace="none",n=10,
- col = heatmapCols,ColSideColors = heatmapColColors,cexRow = 0.25,cexCol = 0.25)
+library(RColorBrewer) +tissues = pData(skin)$SMTSD +heatmapColColors=brewer.pal(12,"Set3")[as.integer(factor(tissues))] +heatmapCols = colorRampPalette(brewer.pal(9, "RdBu"))(50) +yarn::plotHeatmap(skin,normalized=FALSE,log=TRUE,trace="none",n=10, + col = heatmapCols,ColSideColors = heatmapColColors,cexRow = 0.25,cexCol = 0.25)

-
-

-Information

+
+

Information +

-
## R version 4.1.1 (2021-08-10)
-## Platform: x86_64-apple-darwin17.0 (64-bit)
-## Running under: macOS Mojave 10.14.6
-## 
-## Matrix products: default
-## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
-## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
-## 
-## locale:
-## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
-## 
-## attached base packages:
-## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
-## [8] base     
-## 
-## other attached packages:
-## [1] RColorBrewer_1.1-2  netZooR_1.1.16      yarn_1.18.0        
-## [4] pandaR_1.19.5       Biobase_2.52.0      BiocGenerics_0.38.0
-## [7] reticulate_1.22     igraph_1.3.4       
-## 
-## loaded via a namespace (and not attached):
-##   [1] utf8_1.2.2                  proto_1.0.0                
-##   [3] R.utils_2.11.0              RUnit_0.4.32               
-##   [5] tidyselect_1.1.1            RSQLite_2.2.8              
-##   [7] AnnotationDbi_1.54.1        grid_4.1.1                 
-##   [9] BiocParallel_1.26.2         base64url_1.4              
-##  [11] munsell_0.5.0               codetools_0.2-18           
-##  [13] ragg_1.1.3                  preprocessCore_1.54.0      
-##  [15] chron_2.3-56                pbdZMQ_0.3-5               
-##  [17] colorspace_2.0-2            Category_2.58.0            
-##  [19] filelock_1.0.2              highr_0.9                  
-##  [21] dplR_1.7.2                  knitr_1.36                 
-##  [23] uuid_0.1-4                  stats4_4.1.1               
-##  [25] MatrixGenerics_1.4.3        repr_1.1.3                 
-##  [27] GenomeInfoDbData_1.2.6      bit64_4.0.5                
-##  [29] rhdf5_2.36.0                rprojroot_2.0.2            
-##  [31] downloader_0.4              vctrs_0.3.8                
-##  [33] generics_0.1.0              xfun_0.26                  
-##  [35] BiocFileCache_2.0.0         R6_2.5.1                   
-##  [37] doParallel_1.0.16           GenomeInfoDb_1.28.4        
-##  [39] illuminaio_0.34.0           RJSONIO_1.3-1.6            
-##  [41] locfit_1.5-9.4              bitops_1.0-7               
-##  [43] rhdf5filters_1.4.0          cachem_1.0.6               
-##  [45] reshape_0.8.8               DelayedArray_0.18.0        
-##  [47] assertthat_0.2.1            BiocIO_1.2.0               
-##  [49] scales_1.1.1                nnet_7.3-16                
-##  [51] gtable_0.3.0                rlang_0.4.11               
-##  [53] genefilter_1.74.0           systemfonts_1.0.2          
-##  [55] splines_4.1.1               rtracklayer_1.52.1         
-##  [57] GEOquery_2.60.0             hexbin_1.28.2              
-##  [59] reshape2_1.4.4              yaml_2.2.1                 
-##  [61] backports_1.2.1             GenomicFeatures_1.44.2     
-##  [63] RBGL_1.68.0                 tools_4.1.1                
-##  [65] nor1mix_1.3-0               ggplot2_3.3.6              
-##  [67] ellipsis_0.3.2              gplots_3.1.1               
-##  [69] jquerylib_0.1.4             ggdendro_0.1.22            
-##  [71] siggenes_1.66.0             STRINGdb_2.4.1             
-##  [73] gsubfn_0.7                  hash_2.2.6.1               
-##  [75] Rcpp_1.0.7                  plyr_1.8.6                 
-##  [77] base64enc_0.1-3             sparseMatrixStats_1.4.2    
-##  [79] progress_1.2.2              zlibbioc_1.38.0            
-##  [81] purrr_0.3.4                 RCurl_1.98-1.5             
-##  [83] prettyunits_1.1.1           sqldf_0.4-11               
-##  [85] openssl_1.4.5               bumphunter_1.34.0          
-##  [87] S4Vectors_0.30.2            cluster_2.1.2              
-##  [89] SummarizedExperiment_1.22.0 fs_1.5.0                   
-##  [91] magrittr_2.0.1              data.table_1.14.2          
-##  [93] matrixStats_0.61.0          hms_1.1.1                  
-##  [95] evaluate_0.14               xtable_1.8-4               
-##  [97] XML_3.99-0.8                mclust_5.4.7               
-##  [99] IRanges_2.26.0              compiler_4.1.1             
-## [101] biomaRt_2.48.3              minfi_1.38.0               
-## [103] tibble_3.1.5                RCy3_2.12.4                
-## [105] KernSmooth_2.23-20          crayon_1.4.1               
-## [107] R.oo_1.24.0                 htmltools_0.5.2            
-## [109] GOstats_2.58.0              mgcv_1.8-36                
-## [111] tzdb_0.1.2                  tidyr_1.1.4                
-## [113] DBI_1.1.1                   dbplyr_2.1.1               
-## [115] MASS_7.3-54                 rappdirs_0.3.3             
-## [117] Matrix_1.3-4                readr_2.0.2                
-## [119] permute_0.9-5               quantro_1.26.0             
-## [121] uchardet_1.1.0              R.methodsS3_1.8.1          
-## [123] quadprog_1.5-8              GenomicRanges_1.44.0       
-## [125] pkgconfig_2.0.3             pkgdown_1.6.1              
-## [127] GenomicAlignments_1.28.0    signal_0.7-7               
-## [129] IRdisplay_1.0               xml2_1.3.2                 
-## [131] foreach_1.5.1               annotate_1.70.0            
-## [133] penalized_0.9-51            bslib_0.3.1                
-## [135] rngtools_1.5                multtest_2.48.0            
-## [137] beanplot_1.2                XVector_0.32.0             
-## [139] AnnotationForge_1.34.0      doRNG_1.8.2                
-## [141] scrime_1.3.5                stringr_1.4.0              
-## [143] digest_0.6.28               vegan_2.5-7                
-## [145] graph_1.70.0                Biostrings_2.60.2          
-## [147] rmarkdown_2.11              base64_2.0                 
-## [149] edgeR_3.34.1                DelayedMatrixStats_1.14.3  
-## [151] GSEABase_1.54.0             restfulr_0.0.13            
-## [153] curl_4.3.2                  Rsamtools_2.8.0            
-## [155] gtools_3.9.2                rjson_0.2.20               
-## [157] lifecycle_1.0.1             nlme_3.1-152               
-## [159] jsonlite_1.7.2              Rhdf5lib_1.14.2            
-## [161] viridisLite_0.4.0           desc_1.4.0                 
-## [163] askpass_1.1                 limma_3.48.3               
-## [165] fansi_0.5.0                 pillar_1.6.3               
-## [167] lattice_0.20-44             plotrix_3.8-2              
-## [169] KEGGREST_1.32.0             fastmap_1.1.0              
-## [171] httr_1.4.2                  survival_3.2-11            
-## [173] GO.db_3.13.0                glue_1.4.2                 
-## [175] png_0.1-7                   iterators_1.0.13           
-## [177] bit_4.0.4                   Rgraphviz_2.36.0           
-## [179] stringi_1.7.5               sass_0.4.0                 
-## [181] HDF5Array_1.20.0            blob_1.2.2                 
-## [183] textshaping_0.3.5           org.Hs.eg.db_3.13.0        
-## [185] caTools_1.18.2              memoise_2.0.0              
-## [187] IRkernel_1.2                dplyr_1.0.7
+sessionInfo()
+
## R version 4.2.2 (2022-10-31)
+## Platform: x86_64-apple-darwin17.0 (64-bit)
+## Running under: macOS Big Sur ... 10.16
+## 
+## Matrix products: default
+## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
+## 
+## locale:
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+## 
+## attached base packages:
+## [1] stats     graphics  grDevices utils     datasets  methods   base     
+## 
+## other attached packages:
+## [1] RColorBrewer_1.1-3  netZooR_1.5.0       matrixcalc_1.0-6   
+## [4] yarn_1.24.0         pandaR_1.19.6       Biobase_2.58.0     
+## [7] BiocGenerics_0.44.0 reticulate_1.32.0   igraph_1.5.1       
+## 
+## loaded via a namespace (and not attached):
+##   [1] utf8_1.2.3                  proto_1.0.0                
+##   [3] RUnit_0.4.32                tidyselect_1.2.0           
+##   [5] RSQLite_2.3.1               AnnotationDbi_1.60.0       
+##   [7] grid_4.2.2                  BiocParallel_1.32.4        
+##   [9] munsell_0.5.0               base64url_1.4              
+##  [11] codetools_0.2-19            ragg_1.2.5                 
+##  [13] preprocessCore_1.60.1       chron_2.3-61               
+##  [15] pbdZMQ_0.3-10               colorspace_2.1-0           
+##  [17] Category_2.64.0             filelock_1.0.2             
+##  [19] knitr_1.44                  uuid_1.1-1                 
+##  [21] rstudioapi_0.15.0           stats4_4.2.2               
+##  [23] MatrixGenerics_1.10.0       repr_1.1.6                 
+##  [25] GenomeInfoDbData_1.2.9      farver_2.1.1               
+##  [27] bit64_4.0.5                 rhdf5_2.42.0               
+##  [29] rprojroot_2.0.3             downloader_0.4             
+##  [31] vctrs_0.6.3                 generics_0.1.3             
+##  [33] xfun_0.40                   BiocFileCache_2.6.0        
+##  [35] R6_2.5.1                    doParallel_1.0.17          
+##  [37] GenomeInfoDb_1.34.4         illuminaio_0.40.0          
+##  [39] RJSONIO_1.3-1.8             locfit_1.5-9.8             
+##  [41] bitops_1.0-7                rhdf5filters_1.10.0        
+##  [43] cachem_1.0.8                reshape_0.8.9              
+##  [45] DelayedArray_0.24.0         assertthat_0.2.1           
+##  [47] BiocIO_1.8.0                scales_1.2.1               
+##  [49] nnet_7.3-19                 gtable_0.3.4               
+##  [51] processx_3.8.2              rlang_1.1.1                
+##  [53] genefilter_1.80.2           systemfonts_1.0.4          
+##  [55] splines_4.2.2               rtracklayer_1.58.0         
+##  [57] GEOquery_2.66.0             hexbin_1.28.3              
+##  [59] checkmate_2.2.0             abind_1.4-5                
+##  [61] reshape2_1.4.4              yaml_2.3.7                 
+##  [63] backports_1.4.1             GenomicFeatures_1.50.3     
+##  [65] RBGL_1.74.0                 tensorA_0.36.2             
+##  [67] tools_4.2.2                 nor1mix_1.3-0              
+##  [69] ggplot2_3.4.3               gplots_3.1.3               
+##  [71] posterior_1.4.1             jquerylib_0.1.4            
+##  [73] ggdendro_0.1.23             siggenes_1.72.0            
+##  [75] STRINGdb_2.10.0             gsubfn_0.7                 
+##  [77] hash_2.2.6.3                Rcpp_1.0.11                
+##  [79] plyr_1.8.8                  base64enc_0.1-3            
+##  [81] sparseMatrixStats_1.10.0    progress_1.2.2             
+##  [83] zlibbioc_1.44.0             purrr_1.0.2                
+##  [85] RCurl_1.98-1.12             ps_1.7.5                   
+##  [87] prettyunits_1.1.1           sqldf_0.4-11               
+##  [89] openssl_2.1.0               bumphunter_1.40.0          
+##  [91] S4Vectors_0.36.1            cluster_2.1.4              
+##  [93] SummarizedExperiment_1.28.0 fs_1.6.3                   
+##  [95] magrittr_2.0.3              data.table_1.14.8          
+##  [97] matrixStats_1.0.0           hms_1.1.3                  
+##  [99] evaluate_0.21               xtable_1.8-4               
+## [101] XML_3.99-0.14               mclust_6.0.0               
+## [103] IRanges_2.32.0              compiler_4.2.2             
+## [105] biomaRt_2.54.0              minfi_1.44.0               
+## [107] tibble_3.2.1                RCy3_2.18.0                
+## [109] KernSmooth_2.23-22          crayon_1.5.2               
+## [111] htmltools_0.5.6             GOstats_2.64.0             
+## [113] mgcv_1.9-0                  tzdb_0.4.0                 
+## [115] tidyr_1.3.0                 DBI_1.1.3                  
+## [117] dbplyr_2.3.3                MASS_7.3-60                
+## [119] rappdirs_0.3.3              Matrix_1.6-1.1             
+## [121] readr_2.1.4                 permute_0.9-7              
+## [123] cli_3.6.1                   quantro_1.32.0             
+## [125] uchardet_1.1.1              quadprog_1.5-8             
+## [127] parallel_4.2.2              GenomicRanges_1.50.2       
+## [129] pkgconfig_2.0.3             pkgdown_2.0.7              
+## [131] GenomicAlignments_1.34.0    IRdisplay_1.1              
+## [133] xml2_1.3.5                  foreach_1.5.2              
+## [135] annotate_1.76.0             penalized_0.9-52           
+## [137] bslib_0.5.1                 rngtools_1.5.2             
+## [139] multtest_2.54.0             beanplot_1.3.1             
+## [141] XVector_0.38.0              AnnotationForge_1.40.0     
+## [143] doRNG_1.8.6                 scrime_1.3.5               
+## [145] distributional_0.3.2        stringr_1.5.0              
+## [147] digest_0.6.33               vegan_2.6-4                
+## [149] graph_1.76.0                Biostrings_2.66.0          
+## [151] rmarkdown_2.25              base64_2.0.1               
+## [153] edgeR_3.40.1                DelayedMatrixStats_1.20.0  
+## [155] GSEABase_1.60.0             restfulr_0.0.15            
+## [157] curl_5.0.2                  Rsamtools_2.14.0           
+## [159] gtools_3.9.4                rjson_0.2.21               
+## [161] lifecycle_1.0.3             nlme_3.1-163               
+## [163] jsonlite_1.8.7              cmdstanr_0.6.1.9000        
+## [165] Rhdf5lib_1.20.0             viridisLite_0.4.2          
+## [167] desc_1.4.2                  askpass_1.2.0              
+## [169] limma_3.54.2                fansi_1.0.4                
+## [171] pillar_1.9.0                lattice_0.21-8             
+## [173] plotrix_3.8-2               KEGGREST_1.38.0            
+## [175] fastmap_1.1.1               httr_1.4.7                 
+## [177] survival_3.5-7              GO.db_3.16.0               
+## [179] glue_1.6.2                  png_0.1-8                  
+## [181] iterators_1.0.14            bit_4.0.5                  
+## [183] Rgraphviz_2.42.0            stringi_1.7.12             
+## [185] sass_0.4.7                  HDF5Array_1.26.0           
+## [187] blob_1.2.4                  textshaping_0.3.6          
+## [189] org.Hs.eg.db_3.16.0         caTools_1.18.2             
+## [191] memoise_2.0.1               IRkernel_1.3.2             
+## [193] dplyr_1.1.3
-
- - - +
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

+
+

+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.7.

-
+
+ diff --git a/docs/articles/yarn_files/figure-html/checkMisAnnotation-1.png b/docs/articles/yarn_files/figure-html/checkMisAnnotation-1.png index ab5670ea..a2f96cfa 100644 Binary files a/docs/articles/yarn_files/figure-html/checkMisAnnotation-1.png and b/docs/articles/yarn_files/figure-html/checkMisAnnotation-1.png differ diff --git a/docs/articles/yarn_files/figure-html/checkTissuesToMerge-1.png b/docs/articles/yarn_files/figure-html/checkTissuesToMerge-1.png index 3e36ccef..ef93981b 100644 Binary files a/docs/articles/yarn_files/figure-html/checkTissuesToMerge-1.png and b/docs/articles/yarn_files/figure-html/checkTissuesToMerge-1.png differ diff --git a/docs/articles/yarn_files/figure-html/density-1.png b/docs/articles/yarn_files/figure-html/density-1.png index 3df9cef0..0b8bd1b2 100644 Binary files a/docs/articles/yarn_files/figure-html/density-1.png and b/docs/articles/yarn_files/figure-html/density-1.png differ diff --git a/docs/articles/yarn_files/figure-html/density-2.png b/docs/articles/yarn_files/figure-html/density-2.png index bcc5633d..8bdf7137 100644 Binary files a/docs/articles/yarn_files/figure-html/density-2.png and b/docs/articles/yarn_files/figure-html/density-2.png differ diff --git a/docs/articles/yarn_files/figure-html/unnamed-chunk-3-1.png b/docs/articles/yarn_files/figure-html/unnamed-chunk-3-1.png index 69826fa8..511c3218 100644 Binary files a/docs/articles/yarn_files/figure-html/unnamed-chunk-3-1.png and b/docs/articles/yarn_files/figure-html/unnamed-chunk-3-1.png differ diff --git a/docs/articles/yarn_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/yarn_files/figure-html/unnamed-chunk-4-1.png index ed980e4e..44accae8 100644 Binary files a/docs/articles/yarn_files/figure-html/unnamed-chunk-4-1.png and b/docs/articles/yarn_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/docs/articles/yarn_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/yarn_files/figure-html/unnamed-chunk-5-1.png index 169dddc7..e8763cc3 100644 Binary files a/docs/articles/yarn_files/figure-html/unnamed-chunk-5-1.png and b/docs/articles/yarn_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/docs/authors.html b/docs/authors.html index db9b337b..79792e6d 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -1,234 +1,132 @@ - - - - - - - -Citation and Authors • netZooR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Authors and Citation • netZooR + Skip to contents + +
-
-
-

Citation

- Source: inst/CITATION +
+

Authors and Citation

-

Ben Guebila, M, Wang, T., Quackenbush, J. The Network Zoo: a multilingual package for the inference and analysis of biological networks bioRxiv, 2022

- 
@Article{,
+    

+      
Authors

+      
+      
  • 
+          
    Marouen Ben Guebila. Author, maintainer. 
+          
    
+        
    • 
+        
  • 
+          
    Tian Wang. Author. 
+          
    
+        
    • 
+        
  • 
+          
    John Platig. Author. 
+          
    
+        
    • 
+        
  • 
+          
    Marieke Kuijjer. Author. 
+          
    
+        
    • 
+        
  • 
+          
    Megha Padi. Author. 
+          
    
+        
    • 
+        
  • 
+          
    Rebekka Burkholz. Author. 
+          
    
+        
    • 
+        
  • 
+          
    Des Weighill. Author. 
+          
    
+        
    • 
+        
  • 
+          
    Kate Shutta. Author. 
+          
    
+        
    • 
+      

+
+    

+      
Citation

+      
Source: inst/CITATION

+
+      
Ben Guebila, M., Wang, T., Lopes-Ramos, C.L., Fanfani, V., Quackenbush, J. The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks Genome Biology, 2023

+      
@Article{,
   title = {The Network Zoo: a multilingual package for the inference and analysis of biological networks},
-  author = {Marouen Ben Guebila and Tian Wang and John Quackenbush},
-  year = {2022},
-  journal = {bioRxiv},
-  doi = {10.1101/2022.05.30.494077},
+  author = {Marouen Ben Guebila and Tian Wang and Camila M Lopes-Ramos and Viola Fanfani and John Quackenbush},
+  year = {2023},
+  journal = {Genome Biology},
+  doi = {10.1186/s13059-023-02877-1},
 }

-
-    

-      
Authors

     

+
-
    -
  • -

    Marouen Ben Guebila. Author, maintainer. -

    -
    • -
  • -

    Tian Wang. Author. -

    -
    • -
  • -

    John Platig. Author. -

    -
    • -
  • -

    Marieke Kuijjer. Author. -

    -
    • -
  • -

    Megha Padi. Author. -

    -
    • -
  • -

    Rebekka Burkholz. Author. -

    -
    • -
  • -

    Des Weighill. Author. -

    -
    • -
  • -

    Kate Shutta. Contributor. -

    -
    • -
- -
+
+

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill, Kate Shutta.

- - -
-
-

Developed by Marouen Ben Guebila, Tian Wang, John Platig, Marieke Kuijjer, Megha Padi, Rebekka Burkholz, Des Weighill.

-
- -
-

Site built with pkgdown 1.6.1.

+
+

Site built with pkgdown 2.0.7.

-
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+
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J,Z="x"===y?gt:bt,tt="x"===y?mt:_t,et=A[w],it="y"===w?"height":"width",nt=et+m[Z],st=et-m[tt],ot=-1!==[gt,bt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=ae(t,e,i);return n>i?i:n}(at,et,lt):ae(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function $e(t,e,i){void 0===i&&(i=!1);var n,s,o=zt(e),r=zt(e)&&function(t){var e=t.getBoundingClientRect(),i=Xt(e.width)/t.offsetWidth||1,n=Xt(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=ie(e),l=Gt(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==Wt(e)||Ee(a))&&(c=(n=e)!==Bt(n)&&zt(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:we(n)),zt(e)?((h=Gt(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ae(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function We(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Fe(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(B.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=Q.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=Q.find(ti);for(const i of e){const e=hi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Xe,Ye].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ze)?this:Q.prev(this,Ze)[0]||Q.next(this,Ze)[0]||Q.findOne(Ze,t.delegateTarget.parentNode),o=hi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}P.on(document,Ge,Ze,hi.dataApiKeydownHandler),P.on(document,Ge,ei,hi.dataApiKeydownHandler),P.on(document,Ue,hi.clearMenus),P.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),P.on(document,Ue,Ze,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top",fi="padding-right",pi="margin-right";class gi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,fi,(e=>e+t)),this._setElementAttributes(di,fi,(e=>e+t)),this._setElementAttributes(ui,pi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,fi),this._resetElementAttributes(di,fi),this._resetElementAttributes(ui,pi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&B.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=B.getDataAttribute(t,e);null!==i?(B.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of Q.find(t,this._element))e(i)}}const mi="show",_i="mousedown.bs.backdrop",bi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},vi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class yi extends F{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"backdrop"}show(t){if(!this._config.isVisible)return void m(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(mi),this._emulateAnimation((()=>{m(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(mi),this._emulateAnimation((()=>{this.dispose(),m(t)}))):m(t)}dispose(){this._isAppended&&(P.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),P.on(t,_i,(()=>{m(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const wi=".bs.focustrap",Ai="backward",Ei={autofocus:!0,trapElement:null},Ti={autofocus:"boolean",trapElement:"element"};class Ci extends F{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return Ei}static get DefaultType(){return Ti}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),P.off(document,wi),P.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),P.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,P.off(document,wi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=Q.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===Ai?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ai:"forward")}}const Oi="hidden.bs.modal",xi="show.bs.modal",ki="modal-open",Li="show",Di="modal-static",Si={backdrop:!0,focus:!0,keyboard:!0},Ii={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class Ni extends z{constructor(t,e){super(t,e),this._dialog=Q.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new gi,this._addEventListeners()}static get Default(){return Si}static get DefaultType(){return Ii}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||P.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(ki),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(P.trigger(this._element,"hide.bs.modal").defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(Li),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])P.off(t,".bs.modal");this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new yi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ci({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=Q.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(Li),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,P.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){P.on(this._element,"keydown.dismiss.bs.modal",(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),P.on(window,"resize.bs.modal",(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),P.on(this._element,"mousedown.dismiss.bs.modal",(t=>{P.one(this._element,"click.dismiss.bs.modal",(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(ki),this._resetAdjustments(),this._scrollBar.reset(),P.trigger(this._element,Oi)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(P.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Di)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Di),this._queueCallback((()=>{this._element.classList.remove(Di),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Ni.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}P.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),P.one(e,xi,(t=>{t.defaultPrevented||P.one(e,Oi,(()=>{a(this)&&this.focus()}))}));const i=Q.findOne(".modal.show");i&&Ni.getInstance(i).hide(),Ni.getOrCreateInstance(e).toggle(this)})),q(Ni),g(Ni);const Pi="show",ji="showing",Mi="hiding",Hi=".offcanvas.show",$i="hidePrevented.bs.offcanvas",Wi="hidden.bs.offcanvas",Bi={backdrop:!0,keyboard:!0,scroll:!1},Fi={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class zi extends z{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Bi}static get DefaultType(){return Fi}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||P.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new gi).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(ji),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Pi),this._element.classList.remove(ji),P.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(P.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(Mi),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Pi,Mi),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new gi).reset(),P.trigger(this._element,Wi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new yi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():P.trigger(this._element,$i)}:null})}_initializeFocusTrap(){return new Ci({trapElement:this._element})}_addEventListeners(){P.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():P.trigger(this._element,$i))}))}static jQueryInterface(t){return this.each((function(){const e=zi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}P.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;P.one(e,Wi,(()=>{a(this)&&this.focus()}));const i=Q.findOne(Hi);i&&i!==e&&zi.getInstance(i).hide(),zi.getOrCreateInstance(e).toggle(this)})),P.on(window,"load.bs.offcanvas.data-api",(()=>{for(const t of Q.find(Hi))zi.getOrCreateInstance(t).show()})),P.on(window,"resize.bs.offcanvas",(()=>{for(const t of Q.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&zi.getOrCreateInstance(t).hide()})),q(zi),g(zi);const qi=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Ri=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Vi=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Ki=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!qi.has(i)||Boolean(Ri.test(t.nodeValue)||Vi.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Qi={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Xi={allowList:Qi,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Yi={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Ui={entry:"(string|element|function|null)",selector:"(string|element)"};class Gi extends F{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Ui)}_setContent(t,e,i){const n=Q.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Ki(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Ji=new Set(["sanitize","allowList","sanitizeFn"]),Zi="fade",tn="show",en=".modal",nn="hide.bs.modal",sn="hover",on="focus",rn={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},an={allowList:Qi,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover 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object[0] : object\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(object)\n }\n\n return null\n}\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])')\n\n if (!closedDetails) {\n return elementIsVisible\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary')\n if (summary && summary.parentNode !== closedDetails) {\n return false\n }\n\n if (summary === null) {\n return false\n }\n }\n\n return elementIsVisible\n}\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true\n }\n\n if (element.classList.contains('disabled')) {\n return true\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode()\n return root instanceof ShadowRoot ? root : null\n }\n\n if (element instanceof ShadowRoot) {\n return element\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null\n }\n\n return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery\n }\n\n return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback()\n }\n })\n }\n\n DOMContentLoadedCallbacks.push(callback)\n } else {\n callback()\n }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery()\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME\n const JQUERY_NO_CONFLICT = $.fn[name]\n $.fn[name] = plugin.jQueryInterface\n $.fn[name].Constructor = plugin\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT\n return plugin.jQueryInterface\n }\n }\n })\n}\n\nconst execute = callback => {\n if (typeof callback === 'function') {\n callback()\n }\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback)\n return\n }\n\n const durationPadding = 5\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n let called = false\n\n const handler = ({ target }) => {\n if (target !== transitionElement) {\n return\n }\n\n called = true\n transitionElement.removeEventListener(TRANSITION_END, handler)\n execute(callback)\n }\n\n transitionElement.addEventListener(TRANSITION_END, handler)\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement)\n }\n }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length\n let index = list.indexOf(activeElement)\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n }\n\n index += shouldGetNext ? 1 : -1\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n defineJQueryPlugin,\n execute,\n executeAfterTransition,\n findShadowRoot,\n getElement,\n getElementFromSelector,\n getjQuery,\n getNextActiveElement,\n getSelectorFromElement,\n getTransitionDurationFromElement,\n getUID,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop,\n onDOMContentLoaded,\n reflow,\n triggerTransitionEnd,\n toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n 'click',\n 'dblclick',\n 'mouseup',\n 'mousedown',\n 'contextmenu',\n 'mousewheel',\n 'DOMMouseScroll',\n 'mouseover',\n 'mouseout',\n 'mousemove',\n 'selectstart',\n 'selectend',\n 'keydown',\n 'keypress',\n 'keyup',\n 'orientationchange',\n 'touchstart',\n 'touchmove',\n 'touchend',\n 'touchcancel',\n 'pointerdown',\n 'pointermove',\n 'pointerup',\n 'pointerleave',\n 'pointercancel',\n 'gesturestart',\n 'gesturechange',\n 'gestureend',\n 'focus',\n 'blur',\n 'change',\n 'reset',\n 'select',\n 'submit',\n 'focusin',\n 'focusout',\n 'load',\n 'unload',\n 'beforeunload',\n 'resize',\n 'move',\n 'DOMContentLoaded',\n 'readystatechange',\n 'error',\n 'abort',\n 'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element)\n\n element.uidEvent = uid\n eventRegistry[uid] = eventRegistry[uid] || {}\n\n return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, { delegateTarget: element })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn)\n }\n\n return fn.apply(element, [event])\n }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector)\n\n for (let { target } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue\n }\n\n hydrateObj(event, { delegateTarget: target })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn)\n }\n\n return fn.apply(target, [event])\n }\n }\n }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events)\n .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'\n // todo: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n let typeEvent = getTypeEvent(originalTypeEvent)\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent\n }\n\n return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n return fn.call(this, event)\n }\n }\n }\n\n callable = wrapFunction(callable)\n }\n\n const events = getElementEvents(element)\n const handlers = events[typeEvent] || (events[typeEvent] = {})\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n return\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n const fn = isDelegated ?\n bootstrapDelegationHandler(element, handler, callable) :\n bootstrapHandler(element, callable)\n\n fn.delegationSelector = isDelegated ? handler : null\n fn.callable = callable\n fn.oneOff = oneOff\n fn.uidEvent = uid\n handlers[uid] = fn\n\n element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n if (!fn) {\n return\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {}\n\n for (const handlerKey of Object.keys(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n const event = storeElementEvent[handlerKey]\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '')\n return customEvents[event] || event\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false)\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true)\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n const inNamespace = typeEvent !== originalTypeEvent\n const events = getElementEvents(element)\n const storeElementEvent = events[typeEvent] || {}\n const isNamespace = originalTypeEvent.startsWith('.')\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n return\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n }\n }\n\n for (const keyHandlers of Object.keys(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n const event = storeElementEvent[keyHandlers]\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null\n }\n\n const $ = getjQuery()\n const typeEvent = getTypeEvent(event)\n const inNamespace = event !== typeEvent\n\n let jQueryEvent = null\n let bubbles = true\n let nativeDispatch = true\n let defaultPrevented = false\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args)\n\n $(element).trigger(jQueryEvent)\n bubbles = !jQueryEvent.isPropagationStopped()\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n defaultPrevented = jQueryEvent.isDefaultPrevented()\n }\n\n let evt = new Event(event, { bubbles, cancelable: true })\n evt = hydrateObj(evt, args)\n\n if (defaultPrevented) {\n evt.preventDefault()\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt)\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault()\n }\n\n return evt\n }\n}\n\nfunction hydrateObj(obj, meta) {\n for (const [key, value] of Object.entries(meta || {})) {\n try {\n obj[key] = value\n } catch {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value\n }\n })\n }\n }\n\n return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map()\n\nexport default {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map())\n }\n\n const instanceMap = elementMap.get(element)\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n return\n }\n\n instanceMap.set(key, instance)\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null\n }\n\n return null\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return\n }\n\n const instanceMap = elementMap.get(element)\n\n instanceMap.delete(key)\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element)\n }\n }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true\n }\n\n if (value === 'false') {\n return false\n }\n\n if (value === Number(value).toString()) {\n return Number(value)\n }\n\n if (value === '' || value === 'null') {\n return null\n }\n\n if (typeof value !== 'string') {\n return value\n }\n\n try {\n return JSON.parse(decodeURIComponent(value))\n } catch {\n return value\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {}\n }\n\n const attributes = {}\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '')\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n attributes[pureKey] = normalizeData(element.dataset[key])\n }\n\n return attributes\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isElement, toType } from './index'\nimport Manipulator from '../dom/manipulator'\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {}\n }\n\n static get DefaultType() {\n return {}\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!')\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n return config\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n }\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const property of Object.keys(configTypes)) {\n const expectedTypes = configTypes[property]\n const value = config[property]\n const valueType = isElement(value) ? 'element' : toType(value)\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(\n `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n )\n }\n }\n }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data'\nimport { executeAfterTransition, getElement } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Config from './util/config'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.2.2'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super()\n\n element = getElement(element)\n if (!element) {\n return\n }\n\n this._element = element\n this._config = this._getConfig(config)\n\n Data.set(this._element, this.constructor.DATA_KEY, this)\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY)\n EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated)\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY)\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n }\n\n static get VERSION() {\n return VERSION\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`\n }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport { getElementFromSelector, isDisabled } from './index'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`\n const name = component.NAME\n\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n const target = getElementFromSelector(this) || this.closest(`.${name}`)\n const instance = component.getOrCreateInstance(target)\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]()\n })\n}\n\nexport {\n enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n if (closeEvent.defaultPrevented) {\n return\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n }\n\n // Private\n _destroyElement() {\n this._element.remove()\n EventHandler.trigger(this._element, EVENT_CLOSED)\n this.dispose()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this)\n\n if (config === 'toggle') {\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n event.preventDefault()\n\n const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n const data = Button.getOrCreateInstance(button)\n\n data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible } from '../util/index'\n\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector)\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector))\n },\n\n parents(element, selector) {\n const parents = []\n let ancestor = element.parentNode.closest(selector)\n\n while (ancestor) {\n parents.push(ancestor)\n ancestor = ancestor.parentNode.closest(selector)\n }\n\n return parents\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous]\n }\n\n previous = previous.previousElementSibling\n }\n\n return []\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling\n\n while (next) {\n if (next.matches(selector)) {\n return [next]\n }\n\n next = next.nextElementSibling\n }\n\n return []\n },\n\n focusableChildren(element) {\n const focusables = [\n 'a',\n 'button',\n 'input',\n 'textarea',\n 'select',\n 'details',\n '[tabindex]',\n '[contenteditable=\"true\"]'\n ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Config from './config'\nimport EventHandler from '../dom/event-handler'\nimport { execute } from './index'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n}\n\nconst DefaultType = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super()\n this._element = element\n\n if (!element || !Swipe.isSupported()) {\n return\n }\n\n this._config = this._getConfig(config)\n this._deltaX = 0\n this._supportPointerEvents = Boolean(window.PointerEvent)\n this._initEvents()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY)\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX\n\n return\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX\n }\n\n this._handleSwipe()\n execute(this._config.endCallback)\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ?\n 0 :\n event.touches[0].clientX - this._deltaX\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX)\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return\n }\n\n const direction = absDeltaX / this._deltaX\n\n this._deltaX = 0\n\n if (!direction) {\n return\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n defineJQueryPlugin,\n getElementFromSelector,\n getNextActiveElement,\n isRTL,\n isVisible,\n reflow,\n triggerTransitionEnd\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport Swipe from './util/swipe'\nimport BaseComponent from './base-component'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n}\n\nconst DefaultType = {\n interval: '(number|boolean)', // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._interval = null\n this._activeElement = null\n this._isSliding = false\n this.touchTimeout = null\n this._swipeHelper = null\n\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n this._addEventListeners()\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT)\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next()\n }\n }\n\n prev() {\n this._slide(ORDER_PREV)\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element)\n }\n\n this._clearInterval()\n }\n\n cycle() {\n this._clearInterval()\n this._updateInterval()\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n return\n }\n\n this.cycle()\n }\n\n to(index) {\n const items = this._getItems()\n if (index > items.length - 1 || index < 0) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n return\n }\n\n const activeIndex = this._getItemIndex(this._getActive())\n if (activeIndex === index) {\n return\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n this._slide(order, items[index])\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose()\n }\n\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval\n return config\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners()\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause()\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout)\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n }\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n }\n\n this._swipeHelper = new Swipe(this._element, swipeConfig)\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return\n }\n\n const direction = KEY_TO_DIRECTION[event.key]\n if (direction) {\n event.preventDefault()\n this._slide(this._directionToOrder(direction))\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element)\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n activeIndicator.removeAttribute('aria-current')\n\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n newActiveIndicator.setAttribute('aria-current', 'true')\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive()\n\n if (!element) {\n return\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n this._config.interval = elementInterval || this._config.defaultInterval\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return\n }\n\n const activeElement = this._getActive()\n const isNext = order === ORDER_NEXT\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n if (nextElement === activeElement) {\n return\n }\n\n const nextElementIndex = this._getItemIndex(nextElement)\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n })\n }\n\n const slideEvent = triggerEvent(EVENT_SLIDE)\n\n if (slideEvent.defaultPrevented) {\n return\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // todo: change tests that use empty divs to avoid this check\n return\n }\n\n const isCycling = Boolean(this._interval)\n this.pause()\n\n this._isSliding = true\n\n this._setActiveIndicatorElement(nextElementIndex)\n this._activeElement = nextElement\n\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n nextElement.classList.add(orderClassName)\n\n reflow(nextElement)\n\n activeElement.classList.add(directionalClassName)\n nextElement.classList.add(directionalClassName)\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName)\n nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n this._isSliding = false\n\n triggerEvent(EVENT_SLID)\n }\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n if (isCycling) {\n this.cycle()\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE)\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element)\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval)\n this._interval = null\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config)\n\n if (typeof config === 'number') {\n data.to(config)\n return\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n const target = getElementFromSelector(this)\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return\n }\n\n event.preventDefault()\n\n const carousel = Carousel.getOrCreateInstance(target)\n const slideIndex = this.getAttribute('data-bs-slide-to')\n\n if (slideIndex) {\n carousel.to(slideIndex)\n carousel._maybeEnableCycle()\n return\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next()\n carousel._maybeEnableCycle()\n return\n }\n\n carousel.prev()\n carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel)\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n defineJQueryPlugin,\n getElement,\n getElementFromSelector,\n getSelectorFromElement,\n reflow\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n parent: null,\n toggle: true\n}\n\nconst DefaultType = {\n parent: '(null|element)',\n toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isTransitioning = false\n this._triggerArray = []\n\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n for (const elem of toggleList) {\n const selector = getSelectorFromElement(elem)\n const filterElement = SelectorEngine.find(selector)\n .filter(foundElement => foundElement === this._element)\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem)\n }\n }\n\n this._initializeChildren()\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n }\n\n if (this._config.toggle) {\n this.toggle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide()\n } else {\n this.show()\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return\n }\n\n let activeChildren = []\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n .filter(element => element !== this._element)\n .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n if (startEvent.defaultPrevented) {\n return\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide()\n }\n\n const dimension = this._getDimension()\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE)\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n this._element.style[dimension] = 0\n\n this._addAriaAndCollapsedClass(this._triggerArray, true)\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n this._element.style[dimension] = ''\n\n EventHandler.trigger(this._element, EVENT_SHOWN)\n }\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n const scrollSize = `scroll${capitalizedDimension}`\n\n this._queueCallback(complete, this._element, true)\n this._element.style[dimension] = `${this._element[scrollSize]}px`\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n if (startEvent.defaultPrevented) {\n return\n }\n\n const dimension = this._getDimension()\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n for (const trigger of this._triggerArray) {\n const element = getElementFromSelector(trigger)\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false)\n }\n }\n\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE)\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._element.style[dimension] = ''\n\n this._queueCallback(complete, this._element, true)\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW)\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle) // Coerce string values\n config.parent = getElement(config.parent)\n return config\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n for (const element of children) {\n const selected = getElementFromSelector(element)\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected))\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n element.setAttribute('aria-expanded', isOpen)\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {}\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n event.preventDefault()\n }\n\n const selector = getSelectorFromElement(this)\n const selectorElements = SelectorEngine.find(selector)\n\n for (const element of selectorElements) {\n Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (process.env.NODE_ENV !== \"production\") {\n if (!isHTMLElement(arrowElement)) {\n console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n }\n\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref) {\n var x = _ref.x,\n y = _ref.y;\n var win = window;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n if (process.env.NODE_ENV !== \"production\") {\n var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n return transitionProperty.indexOf(property) >= 0;\n })) {\n console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n }\n }\n\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n }\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n }); // Validate the provided modifiers so that the consumer will get warned\n // if one of the modifiers is invalid for any reason\n\n if (process.env.NODE_ENV !== \"production\") {\n var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n var name = _ref.name;\n return name;\n });\n validateModifiers(modifiers);\n\n if (getBasePlacement(state.options.placement) === auto) {\n var flipModifier = state.orderedModifiers.find(function (_ref2) {\n var name = _ref2.name;\n return name === 'flip';\n });\n\n if (!flipModifier) {\n console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n }\n }\n\n var _getComputedStyle = getComputedStyle(popper),\n marginTop = _getComputedStyle.marginTop,\n marginRight = _getComputedStyle.marginRight,\n marginBottom = _getComputedStyle.marginBottom,\n marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n // cause bugs with positioning, so we'll warn the consumer\n\n\n if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n return parseFloat(margin);\n })) {\n console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n }\n }\n\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n var __debug_loops__ = 0;\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (process.env.NODE_ENV !== \"production\") {\n __debug_loops__ += 1;\n\n if (__debug_loops__ > 100) {\n console.error(INFINITE_LOOP_ERROR);\n break;\n }\n }\n\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref3) {\n var name = _ref3.name,\n _ref3$options = _ref3.options,\n options = _ref3$options === void 0 ? {} : _ref3$options,\n effect = _ref3.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport {\n defineJQueryPlugin,\n getElement,\n getNextActiveElement,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n}\n\nconst DefaultType = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._popper = null\n this._parent = this._element.parentNode // dropdown wrapper\n // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n this._inNavbar = this._detectNavbar()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show()\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._createPopper()\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n this._element.focus()\n this._element.setAttribute('aria-expanded', true)\n\n this._menu.classList.add(CLASS_NAME_SHOW)\n this._element.classList.add(CLASS_NAME_SHOW)\n EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n this._completeHide(relatedTarget)\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy()\n }\n\n super.dispose()\n }\n\n update() {\n this._inNavbar = this._detectNavbar()\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n if (this._popper) {\n this._popper.destroy()\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOW)\n this._element.setAttribute('aria-expanded', 'false')\n Manipulator.removeDataAttribute(this._menu, 'popper')\n EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n }\n\n _getConfig(config) {\n config = super._getConfig(config)\n\n if (typeof config.reference === 'object' && !isElement(config.reference) &&\n typeof config.reference.getBoundingClientRect !== 'function'\n ) {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n }\n\n return config\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n }\n\n let referenceElement = this._element\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference)\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference\n }\n\n const popperConfig = this._getPopperConfig()\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW)\n }\n\n _getPlacement() {\n const parentDropdown = this._parent\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static') // todo:v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n }\n }\n\n _selectMenuItem({ key, target }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n if (!items.length) {\n return\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n return\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle)\n if (!context || context._config.autoClose === false) {\n continue\n }\n\n const composedPath = event.composedPath()\n const isMenuTarget = composedPath.includes(context._menu)\n if (\n composedPath.includes(context._element) ||\n (context._config.autoClose === 'inside' && !isMenuTarget) ||\n (context._config.autoClose === 'outside' && isMenuTarget)\n ) {\n continue\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue\n }\n\n const relatedTarget = { relatedTarget: context._element }\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event\n }\n\n context._completeHide(relatedTarget)\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName)\n const isEscapeEvent = event.key === ESCAPE_KEY\n const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return\n }\n\n if (isInput && !isEscapeEvent) {\n return\n }\n\n event.preventDefault()\n\n // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n this :\n (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n if (isUpOrDownEvent) {\n event.stopPropagation()\n instance.show()\n instance._selectMenuItem(event)\n return\n }\n\n if (instance._isShown()) { // else is escape and we check if it is shown\n event.stopPropagation()\n instance.hide()\n getToggleButton.focus()\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n event.preventDefault()\n Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine'\nimport Manipulator from '../dom/manipulator'\nimport { isElement } from './index'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth\n return Math.abs(window.innerWidth - documentWidth)\n }\n\n hide() {\n const width = this.getWidth()\n this._disableOverFlow()\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow')\n this._resetElementAttributes(this._element, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n }\n\n isOverflowing() {\n return this.getWidth() > 0\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow')\n this._element.style.overflow = 'hidden'\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth()\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return\n }\n\n this._saveInitialAttribute(element, styleProperty)\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty)\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue)\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty)\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty)\n return\n }\n\n Manipulator.removeDataAttribute(element, styleProperty)\n element.style.setProperty(styleProperty, value)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector)\n return\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel)\n }\n }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport { execute, executeAfterTransition, getElement, reflow } from './index'\nimport Config from './config'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isAppended = false\n this._element = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._append()\n\n const element = this._getElement()\n if (this._config.isAnimated) {\n reflow(element)\n }\n\n element.classList.add(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n execute(callback)\n })\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n this.dispose()\n execute(callback)\n })\n }\n\n dispose() {\n if (!this._isAppended) {\n return\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n this._element.remove()\n this._isAppended = false\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div')\n backdrop.className = this._config.className\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE)\n }\n\n this._element = backdrop\n }\n\n return this._element\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement)\n return config\n }\n\n _append() {\n if (this._isAppended) {\n return\n }\n\n const element = this._getElement()\n this._config.rootElement.append(element)\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback)\n })\n\n this._isAppended = true\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport SelectorEngine from '../dom/selector-engine'\nimport Config from './config'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n autofocus: 'boolean',\n trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isActive = false\n this._lastTabNavDirection = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus()\n }\n\n EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n this._isActive = true\n }\n\n deactivate() {\n if (!this._isActive) {\n return\n }\n\n this._isActive = false\n EventHandler.off(document, EVENT_KEY)\n }\n\n // Private\n _handleFocusin(event) {\n const { trapElement } = this._config\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement)\n\n if (elements.length === 0) {\n trapElement.focus()\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus()\n } else {\n elements[0].focus()\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin, getElementFromSelector, isRTL, isVisible, reflow } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport SelectorEngine from './dom/selector-engine'\nimport ScrollBarHelper from './util/scrollbar'\nimport BaseComponent from './base-component'\nimport Backdrop from './util/backdrop'\nimport FocusTrap from './util/focustrap'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n backdrop: true,\n focus: true,\n keyboard: true\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._isShown = false\n this._isTransitioning = false\n this._scrollBar = new ScrollBarHelper()\n\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n relatedTarget\n })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._isTransitioning = true\n\n this._scrollBar.hide()\n\n document.body.classList.add(CLASS_NAME_OPEN)\n\n this._adjustDialog()\n\n this._backdrop.show(() => this._showElement(relatedTarget))\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._isShown = false\n this._isTransitioning = true\n this._focustrap.deactivate()\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n }\n\n dispose() {\n for (const htmlElement of [window, this._dialog]) {\n EventHandler.off(htmlElement, EVENT_KEY)\n }\n\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n handleUpdate() {\n this._adjustDialog()\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element)\n }\n\n this._element.style.display = 'block'\n this._element.removeAttribute('aria-hidden')\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.scrollTop = 0\n\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n if (modalBody) {\n modalBody.scrollTop = 0\n }\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_SHOW)\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate()\n }\n\n this._isTransitioning = false\n EventHandler.trigger(this._element, EVENT_SHOWN, {\n relatedTarget\n })\n }\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n event.preventDefault()\n this.hide()\n return\n }\n\n this._triggerBackdropTransition()\n })\n\n EventHandler.on(window, EVENT_RESIZE, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog()\n }\n })\n\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition()\n return\n }\n\n if (this._config.backdrop) {\n this.hide()\n }\n })\n })\n }\n\n _hideModal() {\n this._element.style.display = 'none'\n this._element.setAttribute('aria-hidden', true)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n this._isTransitioning = false\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN)\n this._resetAdjustments()\n this._scrollBar.reset()\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n })\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE)\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const initialOverflowY = this._element.style.overflowY\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden'\n }\n\n this._element.classList.add(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY\n }, this._dialog)\n }, this._dialog)\n\n this._element.focus()\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const scrollbarWidth = this._scrollBar.getWidth()\n const isBodyOverflowing = scrollbarWidth > 0\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = ''\n this._element.style.paddingRight = ''\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](relatedTarget)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n EventHandler.one(target, EVENT_SHOW, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n if (isVisible(this)) {\n this.focus()\n }\n })\n })\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide()\n }\n\n const data = Modal.getOrCreateInstance(target)\n\n data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n defineJQueryPlugin,\n getElementFromSelector,\n isDisabled,\n isVisible\n} from './util/index'\nimport ScrollBarHelper from './util/scrollbar'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\nimport SelectorEngine from './dom/selector-engine'\nimport Backdrop from './util/backdrop'\nimport FocusTrap from './util/focustrap'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n backdrop: true,\n keyboard: true,\n scroll: false\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isShown = false\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._backdrop.show()\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide()\n }\n\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.classList.add(CLASS_NAME_SHOWING)\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate()\n }\n\n this._element.classList.add(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOWING)\n EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n }\n\n this._queueCallback(completeCallBack, this._element, true)\n }\n\n hide() {\n if (!this._isShown) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._focustrap.deactivate()\n this._element.blur()\n this._isShown = false\n this._element.classList.add(CLASS_NAME_HIDING)\n this._backdrop.hide()\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset()\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._queueCallback(completeCallback, this._element, true)\n }\n\n dispose() {\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n return\n }\n\n this.hide()\n }\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop)\n\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (!this._config.keyboard) {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n return\n }\n\n this.hide()\n })\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus()\n }\n })\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide()\n }\n\n const data = Offcanvas.getOrCreateInstance(target)\n data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show()\n }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide()\n }\n }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst uriAttributes = new Set([\n 'background',\n 'cite',\n 'href',\n 'itemtype',\n 'longdesc',\n 'poster',\n 'src',\n 'xlink:href'\n])\n\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i\n\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase()\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue))\n }\n\n return true\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n .some(regex => regex.test(attributeName))\n}\n\nexport const DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml)\n }\n\n const domParser = new window.DOMParser()\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase()\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove()\n\n continue\n }\n\n const attributeList = [].concat(...element.attributes)\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName)\n }\n }\n }\n\n return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer'\nimport { getElement, isElement } from '../util/index'\nimport SelectorEngine from '../dom/selector-engine'\nimport Config from './config'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n allowList: DefaultAllowlist,\n content: {}, // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n}\n\nconst DefaultType = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n}\n\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content)\n .map(config => this._resolvePossibleFunction(config))\n .filter(Boolean)\n }\n\n hasContent() {\n return this.getContent().length > 0\n }\n\n changeContent(content) {\n this._checkContent(content)\n this._config.content = { ...this._config.content, ...content }\n return this\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div')\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector)\n }\n\n const template = templateWrapper.children[0]\n const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '))\n }\n\n return template\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config)\n this._checkContent(config.content)\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template)\n\n if (!templateElement) {\n return\n }\n\n content = this._resolvePossibleFunction(content)\n\n if (!content) {\n templateElement.remove()\n return\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement)\n return\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content)\n return\n }\n\n templateElement.textContent = content\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg(this) : arg\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = ''\n templateElement.append(element)\n return\n }\n\n templateElement.textContent = element.textContent\n }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport { defineJQueryPlugin, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index'\nimport { DefaultAllowlist } from './util/sanitizer'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport BaseComponent from './base-component'\nimport TemplateFactory from './util/template-factory'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 0],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' +\n '
' +\n '
' +\n '
',\n title: '',\n trigger: 'hover focus'\n}\n\nconst DefaultType = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n }\n\n super(element, config)\n\n // Private\n this._isEnabled = true\n this._timeout = 0\n this._isHovered = null\n this._activeTrigger = {}\n this._popper = null\n this._templateFactory = null\n this._newContent = null\n\n // Protected\n this.tip = null\n\n this._setListeners()\n\n if (!this._config.selector) {\n this._fixTitle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n enable() {\n this._isEnabled = true\n }\n\n disable() {\n this._isEnabled = false\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled\n }\n\n toggle() {\n if (!this._isEnabled) {\n return\n }\n\n this._activeTrigger.click = !this._activeTrigger.click\n if (this._isShown()) {\n this._leave()\n return\n }\n\n this._enter()\n }\n\n dispose() {\n clearTimeout(this._timeout)\n\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n if (this.tip) {\n this.tip.remove()\n }\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n }\n\n this._disposePopper()\n super.dispose()\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements')\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n const shadowRoot = findShadowRoot(this._element)\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return\n }\n\n // todo v6 remove this OR make it optional\n if (this.tip) {\n this.tip.remove()\n this.tip = null\n }\n\n const tip = this._getTipElement()\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n const { container } = this._config\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip)\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n }\n\n if (this._popper) {\n this._popper.update()\n } else {\n this._popper = this._createPopper(tip)\n }\n\n tip.classList.add(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n if (this._isHovered === false) {\n this._leave()\n }\n\n this._isHovered = false\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n hide() {\n if (!this._isShown()) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const tip = this._getTipElement()\n tip.classList.remove(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false\n this._activeTrigger[TRIGGER_FOCUS] = false\n this._activeTrigger[TRIGGER_HOVER] = false\n this._isHovered = null // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n if (!this._isHovered) {\n tip.remove()\n }\n\n this._element.removeAttribute('aria-describedby')\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n\n this._disposePopper()\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n update() {\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle())\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n }\n\n return this.tip\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml()\n\n // todo: remove this check on v6\n if (!tip) {\n return null\n }\n\n tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n // todo: on v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n const tipId = getUID(this.constructor.NAME).toString()\n\n tip.setAttribute('id', tipId)\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE)\n }\n\n return tip\n }\n\n setContent(content) {\n this._newContent = content\n if (this._isShown()) {\n this._disposePopper()\n this.show()\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content)\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n })\n }\n\n return this._templateFactory\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n }\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n }\n\n _isAnimated() {\n return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n }\n\n _createPopper(tip) {\n const placement = typeof this._config.placement === 'function' ?\n this._config.placement.call(this, tip, this._element) :\n this._config.placement\n const attachment = AttachmentMap[placement.toUpperCase()]\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg.call(this._element) : arg\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [\n {\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n },\n {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n },\n {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n }\n }\n ]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n }\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ')\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context.toggle()\n })\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSEENTER) :\n this.constructor.eventName(EVENT_FOCUSIN)\n const eventOut = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSELEAVE) :\n this.constructor.eventName(EVENT_FOCUSOUT)\n\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n context._enter()\n })\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n context._element.contains(event.relatedTarget)\n\n context._leave()\n })\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide()\n }\n }\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title')\n\n if (!title) {\n return\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title)\n }\n\n this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title')\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true\n return\n }\n\n this._isHovered = true\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show()\n }\n }, this._config.delay.show)\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n this._isHovered = false\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide()\n }\n }, this._config.delay.hide)\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout)\n this._timeout = setTimeout(handler, timeout)\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true)\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute]\n }\n }\n\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n }\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container)\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n }\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString()\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString()\n }\n\n return config\n }\n\n _getDelegateConfig() {\n const config = {}\n\n for (const key in this._config) {\n if (this.constructor.Default[key] !== this._config[key]) {\n config[key] = this._config[key]\n }\n }\n\n config.selector = false\n config.trigger = 'manual'\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy()\n this._popper = null\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport Tooltip from './tooltip'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' +\n '
' +\n '

' +\n '
' +\n '
',\n trigger: 'click'\n}\n\nconst DefaultType = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent()\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n }\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content)\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.2): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map()\n this._observableSections = new Map()\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n this._activeTarget = null\n this._observer = null\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n }\n this.refresh() // initialize\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables()\n this._maybeEnableSmoothScroll()\n\n if (this._observer) {\n this._observer.disconnect()\n } else {\n this._observer = this._getNewObserver()\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section)\n }\n }\n\n dispose() {\n this._observer.disconnect()\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n }\n\n return config\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK)\n\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash)\n if (observableSection) {\n event.preventDefault()\n const root = this._rootElement || window\n const height = observableSection.offsetTop - this._element.offsetTop\n if (root.scrollTo) {\n root.scrollTo({ top: height, behavior: 'smooth' })\n return\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height\n }\n })\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n }\n\n return new IntersectionObserver(entries => this._observerCallback(entries), options)\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n this._process(targetElement(entry))\n }\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n this._previousScrollData.parentScrollTop = parentScrollTop\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null\n this._clearActiveClass(targetElement(entry))\n\n continue\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry)\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return\n }\n\n continue\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry)\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map()\n this._observableSections = new Map()\n\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue\n }\n\n const observableSection = SelectorEngine.findOne(anchor.hash, this._element)\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(anchor.hash, anchor)\n this._observableSections.set(anchor.hash, observableSection)\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return\n }\n\n this._clearActiveClass(this._config.target)\n this._activeTarget = target\n target.classList.add(CLASS_NAME_ACTIVE)\n this._activateParents(target)\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n .classList.add(CLASS_NAME_ACTIVE)\n return\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
    and