update manuals and vignette

NAMESPACE

@@ -1,19 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
-export(align.ABF.1)
-export(align.ABF.2)
-export(colMax)
+S3method(print,hyprcoloc)
 export(cred.sets)
 export(hyprcoloc)
-export(ind.snp.score)
-export(p.0)
-export(print.hyprcoloc)
-export(prior)
-export(rapid.align)
-export(rapid.hyprcoloc)
-export(rapid.reg)
-export(regional.ABF)
-export(samp.reduc)
 export(sensitivity.plot)
 import(Matrix, except=c(.__C__atomicVector, tcrossprod, crossprod))
 import(RColorBrewer)

R/hyprcoloc.R

@@ -7,7 +7,7 @@
 #' @param p.1 probability of one trait having a causal variant in the genetic region
 #' @param gamma step probability
 #' @param k number of traits
-#' @export
+#' @noRd
 prior <- function(p.1, gamma, k) {
   if (k == 1) {
     p.1
@@ -26,7 +26,7 @@ prior <- function(p.1, gamma, k) {
 #'
 #' @param j number of traits
 #' @param Q number of SNPs
-#' @export
+#' @noRd
 p.0 <- function(j, Q) {
   (j - 1) * Q - j * (j - 1) / 2 + 1
 }
@@ -35,7 +35,7 @@ p.0 <- function(j, Q) {
 #'
 #' @param Q the number of traits
 #' @param snp.scores vector of per snp contributions to the posterior probability of colocalization
-#' @export
+#' @noRd
 ind.snp.score <- function(Q, snp.scores) {
   loc <- vector("numeric", Q - 1)
   res <- vector("numeric", Q)
@@ -70,7 +70,7 @@ ind.snp.score <- function(Q, snp.scores) {
 #' @param sentinel sentinel variant
 #' @param Zsq matrix of Z-scores squared
 #' @param Wsq matrix of W squared
-#' @export
+#' @noRd
 samp.reduc <- function(Z, W, ld.matrix, trait.cor, sample.overlap, trts, window.size = dim(Z)[1], sentinel = 0, Zsq, Wsq) {
   q <- dim(Z)[1]
   Z <- Z[, trts]
@@ -132,22 +132,25 @@ samp.reduc <- function(Z, W, ld.matrix, trait.cor, sample.overlap, trts, window.
 #' colMax
 #'
 #' @param x data.frame or matrix
-#' @export
+#' @noRd
 colMax <- function(x) {
   apply(x, MARGIN = c(2), max)
 }
 
 
 ################################################################################
-##### Compute credible sets of snps for each clusetr of colocalized traits #####
+##### Compute credible sets of snps for each cluster of colocalized traits #####
 ################################################################################
 
-#' cred.sets
+#' Credible sets
 #'
+#' `cred.sets` computes credible sets of SNPs for each cluster of colocalized traits
+#' @name cred.sets
 #' @param res list of results from hyprcoloc when setting "snpscores = TRUE"
 #' @param value the sum of the probabilities of the snps included in the credible set
+#' @author Christopher Foley <chris.neal.foley@gmail.com> & James Staley <jrstaley95@gmail.com>
 #' @export
-
+#' @md
 cred.sets <- function(res, value = 0.95) {
   clusters <- which(!is.na(res[[1]]$traits))
   results <- vector("list", length(clusters))
@@ -180,9 +183,10 @@ cred.sets <- function(res, value = 0.95) {
 ##### Perform a sensitivity analysis by varying the algorithm (regional and alignment) thresholds and coloclaization prior (prior.c)  #####
 ###########################################################################################################################################
 
-#' sensitivity.plot
+#' Sensitivity plot
 #'
-#' sensitivity.plot is a function which repeatedly calls the hyprcoloc function to compute a similarity matrix which illustrates how strongly clustered/colocalized pairs of traits are across different input thresholds and priors
+#' `sensitivity.plot` is a function which repeatedly calls the hyprcoloc function to compute a similarity matrix which illustrates how strongly clustered/colocalized pairs of traits are across different input thresholds and priors
+#' @name sensitivity.plot
 #' @param effect.est matrix of snp regression coefficients (i.e. regression beta values) in the genomic region
 #' @param effect.se matrix of standard errors associated with the beta values
 #' @param binary.outcomes a binary vector of dimension the number of traits: 1 represents a binary trait 0 otherwise
@@ -205,7 +209,9 @@ cred.sets <- function(res, value = 0.95) {
 #' @param equal.thresholds fix the regional and alignment thresholds to be equal
 #' @param similarity.matrix To be viewed as a similarity matrix. Default FALSE.
 #' @import pheatmap RColorBrewer
+#' @author Christopher Foley <chris.neal.foley@gmail.com> & James Staley <jrstaley95@gmail.com>
 #' @export
+#' @md
 sensitivity.plot <- function(effect.est, effect.se, binary.outcomes = rep(0, dim(effect.est)[2]),
                              trait.subset = c(1:dim(effect.est)[2]), trait.names = c(1:dim(effect.est)[2]),
                              snp.id = c(1:dim(effect.est)[1]), ld.matrix = diag(1, dim(effect.est)[1], dim(effect.est)[1]),
@@ -320,7 +326,7 @@ sensitivity.plot <- function(effect.est, effect.se, binary.outcomes = rep(0, dim
 #' @param prior.1 prior probability of a SNP being associated with one trait
 #' @param prior.2 1 - prior probability of a SNP being associated with an additional trait given that the SNP is associated with at least 1 other trait
 #' @param uniform.priors uniform priors
-#' @export
+#' @noRd
 rapid.reg <- function(Zsq, Wsq, prior.1, prior.2, uniform.priors) {
   m <- dim(Zsq)[2]
   Q <- dim(Zsq)[1]
@@ -380,7 +386,7 @@ rapid.reg <- function(Zsq, Wsq, prior.1, prior.2, uniform.priors) {
 #' @param Zsq matrix of Z-scores squared
 #' @param Wsq matrix of W squared
 #' @param ind.traits are the traits independent or to be treated as independent
-#' @export
+#' @noRd
 regional.ABF <- function(Z, W, snps.clc, rho, trait.cor, sample.overlap, epsilon, reg.thresh, prior.1, prior.2, prior.3, prior.4, flag, test.2, reg.steps, cor.adj.priors, uniform.priors, branch.jump, Zsq, Wsq, ind.traits) {
   m <- dim(Z)[2]
   Q <- dim(Z)[1]
@@ -566,7 +572,7 @@ regional.ABF <- function(Z, W, snps.clc, rho, trait.cor, sample.overlap, epsilon
 #' @param prior.1 prior probability of a SNP being associated with one trait
 #' @param prior.2 1 - prior probability of a SNP being associated with an additional trait given that the SNP is associated with at least 1 other trait
 #' @param uniform.priors uniform priors
-#' @export
+#' @noRd
 rapid.align <- function(Zsq, Wsq, prior.1, prior.2, uniform.priors) {
   m <- dim(Zsq)[2]
   Q <- dim(Zsq)[1]
@@ -629,7 +635,7 @@ rapid.align <- function(Zsq, Wsq, prior.1, prior.2, uniform.priors) {
 #' @param Zsq matrix of Z-scores squared
 #' @param Wsq matrix of W squared
 #' @param ind.traits are the traits independent or to be treated as independent
-#' @export
+#' @noRd
 align.ABF.1 <- function(Z, W, trait.cor, sample.overlap, ld.matrix, epsilon, reg.res, align.thresh, prior.1, prior.2, cor.adj.priors, uniform.priors, Zsq, Wsq, ind.traits) {
   m <- dim(Z)[2]
   Q <- dim(Z)[1]
@@ -737,7 +743,7 @@ align.ABF.1 <- function(Z, W, trait.cor, sample.overlap, ld.matrix, epsilon, reg
 #' @param prior.4 1 - prior probability that trait two co-localises with trait one given traits one and two already share a causal variant and trait one contains a second causal variant
 #' @param cor.adj.priors correlation adjusted priors
 #' @param uniform.priors uniform priors
-#' @export
+#' @noRd
 align.ABF.2 <- function(Z, W, snps.clc, trait.cor, sample.overlap, ld.matrix, epsilon, reg.res, align.thresh, prior.1, prior.2, prior.3, prior.4, cor.adj.priors, uniform.priors) {
   m <- dim(Z)[2]
   Q <- dim(Z)[1]
@@ -886,7 +892,7 @@ align.ABF.2 <- function(Z, W, snps.clc, trait.cor, sample.overlap, ld.matrix, ep
 #' @param prior.1 prior probability of a SNP being associated with one trait
 #' @param prior.2 1 - prior probability of a SNP being associated with an additional trait given that the SNP is associated with at least 1 other trait
 #' @param uniform.priors uniform priors
-#' @export
+#' @noRd
 rapid.hyprcoloc <- function(Zsq, Wsq, prior.1, prior.2, uniform.priors) {
   m <- dim(Zsq)[2]
   Q <- dim(Zsq)[1]
@@ -940,7 +946,8 @@ rapid.hyprcoloc <- function(Zsq, Wsq, prior.1, prior.2, uniform.priors) {
 
 #' HyPrColoc
 #'
-#' hyprcoloc is a function used to identify clusters of colocalized traits and candidate causal SNPs in genomic regions
+#' `hyprcoloc` is a function used to identify clusters of colocalized traits and candidate causal SNPs in genomic regions
+#' @name hyprcoloc
 #' @param effect.est matrix of snp regression coefficients (i.e. regression beta values) in the genomic region
 #' @param effect.se matrix of standard errors associated with the beta values
 #' @param binary.outcomes a binary vector of dimension the number of traits: 1 represents a binary trait 0 otherwise
@@ -965,12 +972,10 @@ rapid.hyprcoloc <- function(Zsq, Wsq, prior.1, prior.2, uniform.priors) {
 #' @param ind.traits are the traits independent or to be treated as independent
 #' @param snpscores output estimated posterior probability explained each SNP
 #' @return A data.frame of HyPrColoc results: each row is a cluster of colocalized traits or is coded NA (if no colocalization is identified)
-#' @return If snpscores=TRUE: additionally returns a list of posterior probability explained by each SNPs and for each cluster of colocalized traits identified
+#' @return If snpscores = TRUE: additionally returns a list of posterior probability explained by each SNPs and for each cluster of colocalized traits identified
 #' @import compiler Rmpfr iterpc
-#' @rawNamespace import(Matrix, except=c(.__C__atomicVector, tcrossprod, crossprod))
 #' @importFrom Rcpp evalCpp
 #' @useDynLib hyprcoloc
-#' @author Christopher N Foley <chris.neal.foley@gmail.com> and James R Staley <jrstaley95@gmail.com>
 #' @examples
 #' # Regression coefficients and standard errors from ten GWAS studies
 #' # (Traits 1-5, 6-8 & 9-10 are the clusters of colocalized traits)
@@ -985,7 +990,9 @@ rapid.hyprcoloc <- function(Zsq, Wsq, prior.1, prior.2, uniform.priors) {
 #'
 #' # Colocalisation analyses
 #' results <- hyprcoloc(betas, ses, trait.names = traits, snp.id = rsid)
+#' @author Christopher Foley <chris.neal.foley@gmail.com> & James Staley <jrstaley95@gmail.com>
 #' @export
+#' @md
 hyprcoloc <- function(effect.est, effect.se, binary.outcomes = rep(0, dim(effect.est)[2]),
                       trait.subset = c(1:dim(effect.est)[2]), trait.names = c(1:dim(effect.est)[2]),
                       snp.id = c(1:dim(effect.est)[1]), ld.matrix = diag(1, dim(effect.est)[1], dim(effect.est)[1]),
@@ -1556,15 +1563,14 @@ hyprcoloc <- function(effect.est, effect.se, binary.outcomes = rep(0, dim(effect
   return(results)
 }
 
-#' Print HyPrColoc
-#'
-#' print method for class "hyprcoloc"
-#' @param x an object of class "hyprcoloc"
-#' @author Christopher N Foley (University of Cambridge) <chris.neal.foley@gmail.com> and James R Staley (University of Bristol) <jrstaley95@gmail.com>
-#' @param ... Other arguments passed to or from other methods
+#' Print hyprcoloc
 #'
-#' @export print.hyprcoloc
+#' print method for class `"hyprcoloc"`.
+#' @name print.hyprcoloc
+#' @param x an object of class `"hyprcoloc"`
+#' @author Christopher Foley <chris.neal.foley@gmail.com> & James Staley <jrstaley95@gmail.com>
 #' @export
+#' @md
 print.hyprcoloc <- function(x, ...) {
   cat("\nCall: \nhyprcoloc")
   cat("\n\nResults:\n")
@@ -1573,4 +1579,4 @@ print.hyprcoloc <- function(x, ...) {
 }
 
 # prevents R check throwing errors about unassigned global variables
-utils::globalVariables(c("colorRampPalette", "combn", "runif"))
+# utils::globalVariables(c("colorRampPalette", "combn", "runif"))

README.md

@@ -5,8 +5,10 @@ Genome-wide association studies (GWAS) have identified thousands of genomic regi
 
 HyPrColoc is an efficient deterministic Bayesian divisive clustering algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously.  
 
-## Function
+## Functions
 * `hyprcoloc`: identifies clusters of colocalized traits and candidate causal SNPs using the HyPrColoc Bayesian divisive clustering algorithm.  
+* `sensitivity_plot`: plots a heatmap to visuale how stable the clusters of colocalized traits are to variations in the algorithms input parameters.  
+* `cred.sets`: computes credible sets of SNPs for each cluster of colocalized traits.  
 
 ## Installation
 ```