Merge pull request #86 from BIGslu/dev_msc

Replace "." with "_" in parameter names
BIGslu · May 31, 2023 · f3381fd · f3381fd
2 parents eb0cda9 + 8e0ff5b
commit f3381fd
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: kimma
 Type: Package
 Title: Kinship In Mixed Model Analysis of RNA-seq
-Version: 1.4.4
+Version: 1.5.0
 Author: Kim Dill-McFarland
 Maintainer: Kim Dill-McFarland <kadm@uw.edu>
 Description: Linear mixed effects models with pairwise kinship for RNA-seq data.

diff --git a/R/extract_lmFit.R b/R/extract_lmFit.R
@@ -36,10 +36,10 @@
 extract_lmFit <- function(design, fit, contrast_mat=NULL,
                           dat_genes=NULL, name_genes="geneName",
                           contrast.mat=NULL, dat.genes=NULL, name.genes=NULL){
-  #Back compatability
-  contrast_mat <- contrast.mat
-  dat_genes <- dat.genes
-  name_genes <- name.genes
+  #Back compatibility
+  if(!is.null(contrast.mat)){contrast_mat <- contrast.mat}
+  if(!is.null(dat.genes)){dat_genes <- dat.genes}
+  if(!is.null(name.genes)){name_genes <- name.genes}
 
   #Empty df to hold results
   pval.result <- data.frame()

diff --git a/R/kimma_cleaning.R b/R/kimma_cleaning.R
@@ -13,20 +13,20 @@
 #' @param weights Matrix or data frame of gene specific weights
 #'
 #' Subset data (optional)
-#' @param subset.var Character list of variable name(s) to filter data by.
-#' @param subset.lvl Character list of variable value(s) or level(s) to filter data to. Must match order of subset.var
-#' @param subset.genes Character vector of genes to include in models.
-#' @param model.lm Character vector of simple linear model version of model provided
-#' @param genotype.name Character string. Used internally for kmFit_eQTL
+#' @param subset_var Character list of variable name(s) to filter data by.
+#' @param subset_lvl Character list of variable value(s) or level(s) to filter data to. Must match order of subset_var
+#' @param subset_genes Character vector of genes to include in models.
+#' @param model_lm Character vector of simple linear model version of model provided
+#' @param genotype_name Character string. Used internally for kmFit_eQTL
 #'
 #' @return Data frame formatted for use in kmFit
 #' @keywords internal
 #' @importFrom tidyselect vars_select_helpers
 
 kimma_cleaning <- function(dat=NULL, kin=NULL, patientID="ptID", libraryID="libID",
                            counts=NULL, meta=NULL, genes=NULL, weights=NULL,
-                           subset.var = NULL, subset.lvl = NULL, subset.genes = NULL,
-                           model.lm = NULL, genotype.name = NULL){
+                           subset_var = NULL, subset_lvl = NULL, subset_genes = NULL,
+                           model_lm = NULL, genotype_name = NULL){
   i <- rowname <- NULL
   #If data are NOT a voom EList, create a mock version
   if(is.null(dat)) {
@@ -135,10 +135,10 @@ kimma_cleaning <- function(dat=NULL, kin=NULL, patientID="ptID", libraryID="libI
   dat.subset <- dat.format
 
   #Subset samples
-  if(!is.null(subset.var)){
-    for(i in 1:length(subset.var)) {
+  if(!is.null(subset_var)){
+    for(i in 1:length(subset_var)) {
       dat.subset$targets <- dplyr::filter(dat.subset$targets,
-                                          get(subset.var[[i]]) %in% subset.lvl[[i]])
+                                          get(subset_var[[i]]) %in% subset_lvl[[i]])
 
       dat.subset$E <- dplyr::select(as.data.frame(dat.subset$E),
                                     rowname,
@@ -153,12 +153,12 @@ kimma_cleaning <- function(dat=NULL, kin=NULL, patientID="ptID", libraryID="libI
   }
 
   #Subset genes
-  if(!is.null(subset.genes)){
+  if(!is.null(subset_genes)){
     dat.subset$E <- dplyr::filter(as.data.frame(dat.subset$E),
-                                  rowname %in% subset.genes)
+                                  rowname %in% subset_genes)
     if(!is.null(dat.subset$weights)){
       dat.subset$weights <- dplyr::filter(as.data.frame(dat.subset$weights),
-                                          rowname %in% subset.genes)
+                                          rowname %in% subset_genes)
     }
   }
 
@@ -239,7 +239,7 @@ kimma_cleaning <- function(dat=NULL, kin=NULL, patientID="ptID", libraryID="libI
     }
 
     ##Missing other variables
-    all_vars <- gsub("expression~", "", model.lm)
+    all_vars <- gsub("expression~", "", model_lm)
     all_vars <- unique(strsplit(all_vars, "\\+|\\*|:")[[1]])
 
     complete <- to.model %>%
@@ -286,10 +286,10 @@ kimma_cleaning <- function(dat=NULL, kin=NULL, patientID="ptID", libraryID="libI
     }
 
     ## Missing data
-    all_vars <- gsub("expression~", "", model.lm)
+    all_vars <- gsub("expression~", "", model_lm)
     all_vars <- unique(strsplit(all_vars, "\\+|\\*|:")[[1]])
 
-    if(!is.null(genotype.name)){ all_vars <- c(all_vars, genotype.name)}
+    if(!is.null(genotype_name)){ all_vars <- c(all_vars, genotype_name)}
 
     complete <- to.model %>%
       dplyr::select(tidyselect::all_of(c(libraryID, all_vars))) %>%

diff --git a/R/kimma_lm.R b/R/kimma_lm.R
@@ -1,28 +1,28 @@
 #' Run kimma linear model
 #'
-#' @param model.lm Character model created in kmFit
-#' @param to.model.gene Data frame formatted in kmFit, subset to gene of interest
+#' @param model_lm Character model created in kmFit
+#' @param to_model_gene Data frame formatted in kmFit, subset to gene of interest
 #' @param gene Character of gene to model
-#' @param use.weights Logical if gene specific weights should be used in model. Default is FALSE
+#' @param use_weights Logical if gene specific weights should be used in model. Default is FALSE
 #' @param metrics Logical if should calculate model fit metrics such as AIC, BIC, R-squared. Default is FALSE
 #'
 #' @return Linear model results data frame for 1 gene
 #' @keywords internal
 
-kimma_lm <- function(model.lm, to.model.gene, gene, use.weights, metrics){
+kimma_lm <- function(model_lm, to_model_gene, gene, use_weights, metrics){
     #Place holder LM results
     p.lm <- NaN
     sigma.lm <- 0
     results.lm <- NULL
-    .GlobalEnv$to.model.gene <- to.model.gene
+    .GlobalEnv$to_model_gene <- to_model_gene
 
     #Fit model
-    if(use.weights){
+    if(use_weights){
       set.seed(42)
-      fit.lm <- stats::lm(model.lm, data=to.model.gene, weights=to.model.gene$gene_weight)
+      fit.lm <- stats::lm(model_lm, data=to_model_gene, weights=to_model_gene$gene_weight)
     } else{
       set.seed(42)
-      fit.lm <- stats::lm(model.lm, data=to.model.gene, weights=NULL)
+      fit.lm <- stats::lm(model_lm, data=to_model_gene, weights=NULL)
     }
 
     p.lm <- broom::tidy(fit.lm)

diff --git a/R/kimma_lme.R b/R/kimma_lme.R
@@ -1,30 +1,30 @@
 #' Run kimma linear mixed effects model
 #'
-#' @param model.lme Character model created in kmFit
-#' @param to.model.gene Data frame formatted in kmFit, subset to gene of interest
+#' @param model_lme Character model created in kmFit
+#' @param to_model_gene Data frame formatted in kmFit, subset to gene of interest
 #' @param gene Character of gene to model
-#' @param use.weights Logical if gene specific weights should be used in model. Default is FALSE
+#' @param use_weights Logical if gene specific weights should be used in model. Default is FALSE
 #' @param metrics Logical if should calculate model fit metrics such as AIC, BIC, R-squared. Default is FALSE
 #'
 #' @return Linear mixed effect results data frame for 1 gene
 #' @keywords internal
 
-kimma_lme <- function(model.lme, to.model.gene, gene, use.weights, metrics){
+kimma_lme <- function(model_lme, to_model_gene, gene, use_weights, metrics){
     rowname <- NULL
     #Place holder LME results
     p.lme <- NaN
     sigma.lme <- 0
     results.lme <- NULL
-    .GlobalEnv$to.model.gene <- to.model.gene
+    .GlobalEnv$to_model_gene <- to_model_gene
 
     #Fit LME model
-    if(use.weights){
+    if(use_weights){
       set.seed(42)
-      fit.lme <- lme4::lmer(model.lme, data=to.model.gene,
-                            weights=to.model.gene$gene_weight)
+      fit.lme <- lme4::lmer(model_lme, data=to_model_gene,
+                            weights=to_model_gene$gene_weight)
     } else{
       set.seed(42)
-      fit.lme <- lme4::lmer(model.lme, data=to.model.gene, weights=NULL)
+      fit.lme <- lme4::lmer(model_lme, data=to_model_gene, weights=NULL)
     }
 
     #Estimate P-value
@@ -62,11 +62,11 @@ kimma_lme <- function(model.lme, to.model.gene, gene, use.weights, metrics){
 
     if(metrics){
       #Calculate R-squared
-      if(use.weights){
-        null <- stats::glm(formula = expression ~ 1, data = to.model.gene,
-                           weights = to.model.gene$gene_weight)
+      if(use_weights){
+        null <- stats::glm(formula = expression ~ 1, data = to_model_gene,
+                           weights = to_model_gene$gene_weight)
       } else{
-        null <- stats::glm(formula = expression ~ 1, data = to.model.gene)
+        null <- stats::glm(formula = expression ~ 1, data = to_model_gene)
       }
 
       L0 <- as.vector(stats::logLik(null))

diff --git a/R/kimma_lmerel.R b/R/kimma_lmerel.R
@@ -1,37 +1,37 @@
 #' Run kimma linear mixed effects model with kinship
 #'
-#' @param model.lme Character model created in kmFit
-#' @param to.model.gene Data frame formatted in kmFit, subset to gene of interest
+#' @param model_lme Character model created in kmFit
+#' @param to_model_gene Data frame formatted in kmFit, subset to gene of interest
 #' @param gene Character of gene to model
-#' @param kin.subset Pairwise kinship matrix
-#' @param use.weights Logical if gene specific weights should be used in model. Default is FALSE
+#' @param kin_subset Pairwise kinship matrix
+#' @param use_weights Logical if gene specific weights should be used in model. Default is FALSE
 #' @param patientID Character of variable name to match dat$targets to kinship row and column names.
 #' @param metrics Logical if should calculate model fit metrics such as AIC, BIC, R-squared. Default is FALSE
 #'
 #' @return Linear mixed effect with kinship results data frame for 1 gene
 #' @keywords internal
 
-kimma_lmerel <- function(model.lme, to.model.gene, gene, kin.subset, use.weights,
+kimma_lmerel <- function(model_lme, to_model_gene, gene, kin_subset, use_weights,
                          patientID, metrics){
   rowname <- NULL
   #Place holder LME results
   p.kin <- NaN
   sigma.kin <- 0
   results.kin <- NULL
-  .GlobalEnv$to.model.gene <- to.model.gene
+  .GlobalEnv$to_model_gene <- to_model_gene
 
   #Format kinship matrix to list
   kin.ls <- list()
-  kin.ls[[patientID]] <- as.matrix(kin.subset)
+  kin.ls[[patientID]] <- as.matrix(kin_subset)
   #Fit kin model
-    if(use.weights){
+    if(use_weights){
       set.seed(42)
-    fit.kin <- lme4qtl::relmatLmer(model.lme, data=to.model.gene,
+    fit.kin <- lme4qtl::relmatLmer(model_lme, data=to_model_gene,
                                    relmat = kin.ls,
-                                   weights=to.model.gene$gene_weight)
+                                   weights=to_model_gene$gene_weight)
   } else{
     set.seed(42)
-    fit.kin <- lme4qtl::relmatLmer(model.lme, data=to.model.gene,
+    fit.kin <- lme4qtl::relmatLmer(model_lme, data=to_model_gene,
                                    relmat = kin.ls)
   }
 
@@ -70,11 +70,11 @@ kimma_lmerel <- function(model.lme, to.model.gene, gene, kin.subset, use.weights
 
   if(metrics){
     #Calculate R-squared
-    if(use.weights){
-      null <- stats::glm(formula = expression ~ 1, data = to.model.gene,
-                         weights = to.model.gene$gene_weight)
+    if(use_weights){
+      null <- stats::glm(formula = expression ~ 1, data = to_model_gene,
+                         weights = to_model_gene$gene_weight)
     } else{
-      null <- stats::glm(formula = expression ~ 1, data = to.model.gene)
+      null <- stats::glm(formula = expression ~ 1, data = to_model_gene)
     }
 
     L0 <- as.vector(stats::logLik(null))