Merge pull request #93 from PF2-pasteur-fr/development

ashr

Author: hvaret

DESCRIPTION

@@ -1,13 +1,13 @@
 Package: SARTools
 Type: Package
 Title: Statistical Analysis of RNA-Seq Tools
-Version: 1.8.0
-Date: 2022-02-17
+Version: 1.8.1
+Date: 2022-03-23
 Author: Marie-Agnes Dillies and Hugo Varet
 Maintainer: Hugo Varet <[email protected]>
 Depends: R (>= 3.3.0),
 	DESeq2 (>= 1.32.0),
-	apeglm (>= 1.14.0),
+	ashr (>= 2.2-54),
 	edgeR (>= 3.34.0),
 	ggplot2 (>= 3.3.0),
 	kableExtra

NAMESPACE

@@ -2,7 +2,7 @@
 
 exportPattern("^[a-zA-Z]")
 import(DESeq2)
-import(apeglm)
+import(ashr)
 import(edgeR)
 import(ggplot2)
 import(kableExtra)

NEWS

@@ -1,3 +1,8 @@
+CHANGES IN VERSION 1.8.1
+------------------------
+	o log2FoldChanges from DESeq2 are now computed using lfcShrink() with type="ashr" as "apeglm"
+	was not compatible with the contrast argument
+
 CHANGES IN VERSION 1.8.0
 ------------------------
 	o log2FoldChanges from DESeq2 are now computed using lfcShrink() with type="apeglm"

R/NAMESPACE.R

@@ -1,6 +1,6 @@
 #' @exportPattern ^[a-zA-Z]
 #' @import DESeq2
-#' @import apeglm
+#' @import ashr
 #' @import edgeR
 #' @import ggplot2
 #' @import kableExtra

R/descriptionPlots.r

@@ -5,11 +5,12 @@
 #' @param counts \code{matrix} of counts
 #' @param group factor vector of the condition from which each sample belongs
 #' @param col colors for the plots (one per biological condition)
-#' @param ggplot_theme ggplot2 theme function (\code{theme_gray()} by default)
+#' @param ggplot_theme ggplot2 theme function (\code{theme_light()} by default)
 #' @return PNG files in the "figures" directory and the matrix of the most expressed sequences
 #' @author Hugo Varet
 
-descriptionPlots <- function(counts, group, col=c("lightblue","orange","MediumVioletRed","SpringGreen"), ggplot_theme=theme_gray()){
+descriptionPlots <- function(counts, group, col=c("lightblue","orange","MediumVioletRed","SpringGreen"), 
+                             ggplot_theme=theme_light()){
   # create the figures directory if does not exist
   if (!I("figures" %in% dir())) dir.create("figures", showWarnings=FALSE)
 

R/exploreCounts.R

@@ -7,13 +7,13 @@
 #' @param typeTrans transformation method for PCA/clustering with DESeq2: \code{"VST"} or \code{"rlog"}
 #' @param gene.selection selection of the features in MDSPlot (\code{"pairwise"} by default)
 #' @param col colors used for the PCA/MDS (one per biological condition)
-#' @param ggplot_theme ggplot2 theme function (\code{theme_gray()} by default)
+#' @param ggplot_theme ggplot2 theme function (\code{theme_light()} by default)
 #' @return A list containing the dds object and the results object
 #' @author Hugo Varet
 
 exploreCounts <- function(object, group, typeTrans="VST", gene.selection="pairwise",
                           col=c("lightblue","orange","MediumVioletRed","SpringGreen"),
-                          ggplot_theme=theme_gray()){
+                          ggplot_theme=theme_light()){
   if (class(object)=="DESeqDataSet"){
     if (typeTrans == "VST") counts.trans <- assay(varianceStabilizingTransformation(object))
     else counts.trans <- assay(rlogTransformation(object))

R/run.DESeq2.r

@@ -39,14 +39,17 @@ run.DESeq2 <- function(counts, target, varInt, batch=NULL,
   for (comp in combn(nlevels(colData(dds)[,varInt]), 2, simplify=FALSE)){
     levelRef <- levels(colData(dds)[,varInt])[comp[1]]
     levelTest <- levels(colData(dds)[,varInt])[comp[2]]
-    res <- results(dds, name=paste(c(varInt, levelTest, "vs", levelRef), collapse="_"),
+    res <- results(dds,
+                   contrast=c(varInt, levelTest, levelRef),
                    pAdjustMethod=pAdjustMethod, cooksCutoff=cooksCutoff,
                    independentFiltering=independentFiltering, alpha=alpha)
-    lfcs <- lfcShrink(dds, res=res, coef=paste(c(varInt, levelTest, "vs", levelRef), collapse="_"), type="apeglm")
+    lfcs <- lfcShrink(dds, res=res,
+                      contrast=c(varInt, levelTest, levelRef),
+                      type="ashr", quiet=TRUE)
     res$log2FoldChange <- lfcs$log2FoldChange
     results[[paste0(levelTest,"_vs_",levelRef)]] <- res
     cat(paste("Comparison", levelTest, "vs", levelRef, "done\n"))
   }
 
-  return(list(dds=dds,results=results,sf=sizeFactors(dds)))
+  return(list(dds=dds, results=results, sf=sizeFactors(dds)))
 }

R/summarizeResults.DESeq2.r

@@ -10,13 +10,13 @@
 #' @param col colors for the plots
 #' @param log2FClim numeric vector containing both upper and lower y-axis limits for all the MA-plots produced (NULL by default to set them automatically)
 #' @param padjlim numeric value between 0 and 1 for the adjusted p-value upper limits for all the volcano plots produced (NULL by default to set them automatically)
-#' @param ggplot_theme ggplot2 theme function (\code{theme_gray()} by default)
+#' @param ggplot_theme ggplot2 theme function (\code{theme_light()} by default)
 #' @return A list containing: (i) a list of \code{data.frames} from \code{exportResults.DESeq2()}, (ii) the table summarizing the independent filtering procedure and (iii) a table summarizing the number of differentially expressed features
 #' @author Hugo Varet
 
 summarizeResults.DESeq2 <- function(out.DESeq2, group, independentFiltering=TRUE, cooksCutoff=TRUE,
                                     alpha=0.05, col=c("lightblue","orange","MediumVioletRed","SpringGreen"),
-                                    log2FClim=NULL, padjlim=NULL, ggplot_theme=theme_gray()){
+                                    log2FClim=NULL, padjlim=NULL, ggplot_theme=theme_light()){
   # create the figures/tables directory if does not exist
   if (!I("figures" %in% dir())) dir.create("figures", showWarnings=FALSE)
   if (!I("tables" %in% dir())) dir.create("tables", showWarnings=FALSE)

R/summarizeResults.edgeR.r

@@ -9,13 +9,13 @@
 #' @param col colors for the plots
 #' @param log2FClim numeric vector containing both upper and lower y-axis limits for all the MA-plots produced (NULL by default to set them automatically)
 #' @param padjlim numeric value between 0 and 1 for the adjusted p-value upper limits for all the volcano plots produced (NULL by default to set them automatically)
-#' @param ggplot_theme ggplot2 theme function (\code{theme_gray()} by default)
+#' @param ggplot_theme ggplot2 theme function (\code{theme_light()} by default)
 #' @return A list containing: (i) a list of \code{data.frames} from \code{exportResults.edgeR()} and (ii) a table summarizing the number of differentially expressed features
 #' @author Hugo Varet
 
 summarizeResults.edgeR <- function(out.edgeR, group, counts, alpha=0.05,
                                    col=c("lightblue","orange","MediumVioletRed","SpringGreen"),
-                                   log2FClim=NULL, padjlim=NULL, ggplot_theme=theme_gray()){  
+                                   log2FClim=NULL, padjlim=NULL, ggplot_theme=theme_light()){  
   # create the figures/tables directory if does not exist
   if (!I("figures" %in% dir())) dir.create("figures", showWarnings=FALSE)
   if (!I("tables" %in% dir())) dir.create("tables", showWarnings=FALSE)

inst/bibliography.bib

@@ -232,3 +232,19 @@ @article{zhu2018
     url = {https://doi.org/10.1093/bioinformatics/bty895},
     eprint = {https://academic.oup.com/bioinformatics/article-pdf/35/12/2084/28839676/bty895.pdf},
 }
+
+@article{ashr,
+    author = {Stephens, Matthew},
+    title = "{False discovery rates: a new deal}",
+    journal = {Biostatistics},
+    volume = {18},
+    number = {2},
+    pages = {275-294},
+    year = {2016},
+    month = {10},
+    abstract = "{We introduce a new Empirical Bayes approach for large-scale hypothesis testing, including estimating false discovery rates (FDRs), and effect sizes. This approach has two key differences from existing approaches to FDR analysis. First, it assumes that the distribution of the actual (unobserved) effects is unimodal, with a mode at 0. This “unimodal assumption” (UA), although natural in many contexts, is not usually incorporated into standard FDR analysis, and we demonstrate how incorporating it brings many benefits. Specifically, the UA facilitates efficient and robust computation—estimating the unimodal distribution involves solving a simple convex optimization problem—and enables more accurate inferences provided that it holds. Second, the method takes as its input two numbers for each test (an effect size estimate and corresponding standard error), rather than the one number usually used (\\$p\\$ value or \\$z\\$ score). When available, using two numbers instead of one helps account for variation in measurement precision across tests. It also facilitates estimation of effects, and unlike standard FDR methods, our approach provides interval estimates (credible regions) for each effect in addition to measures of significance. To provide a bridge between interval estimates and significance measures, we introduce the term “local false sign rate” to refer to the probability of getting the sign of an effect wrong and argue that it is a superior measure of significance than the local FDR because it is both more generally applicable and can be more robustly estimated. Our methods are implemented in an R package ashr available from http://github.com/stephens999/ashr.}",
+    issn = {1465-4644},
+    doi = {10.1093/biostatistics/kxw041},
+    url = {https://doi.org/10.1093/biostatistics/kxw041},
+    eprint = {https://academic.oup.com/biostatistics/article-pdf/18/2/275/11057424/kxw041.pdf},
+}

inst/report_DESeq2.rmd

@@ -226,7 +226,7 @@ Figure 14 shows the volcano plots for the comparisons performed and differential
 
 </center>
 
-Note that the log2(Fold-Changes) are shrunk using the "apeglm" method that has been shown to be more robust than the original "normal" method [@zhu2018].
+Note that the log2(Fold-Changes) are shrunk using the "ashr" method that has been shown to be more robust than the original "normal" method [@ashr].
 
 Full results as well as lists of differentially expressed features are provided in the following text files which can be easily read in a spreadsheet. For each comparison:
 

man/descriptionPlots.Rd

@@ -1,8 +1,8 @@
 ################################################################################
 ### R script to compare several conditions with the SARTools and DESeq2 packages
 ### Hugo Varet
-### November 28th, 2019
-### designed to be executed with SARTools 1.7.4
+### March 23rd, 2022
+### designed to be executed with SARTools 1.8.1
 ################################################################################
 
 ################################################################################

man/exploreCounts.Rd

@@ -1,8 +1,8 @@
 ################################################################################
 ### R script to compare several conditions with the SARTools and DESeq2 packages
 ### Hugo Varet
-### November 28th, 2019
-### designed to be executed with SARTools 1.7.4
+### March 23rd, 2022
+### designed to be executed with SARTools 1.8.1
 ### run "Rscript template_script_DESeq2_CL.r --help" to get some help
 ################################################################################
 

man/summarizeResults.DESeq2.Rd

@@ -1,8 +1,8 @@
 ################################################################################
 ### R script to compare several conditions with the SARTools and edgeR packages
 ### Hugo Varet
-### November 28th, 2019
-### designed to be executed with SARTools 1.7.4
+### March 23rd, 2022
+### designed to be executed with SARTools 1.8.1
 ################################################################################
 
 ################################################################################

man/summarizeResults.edgeR.Rd

@@ -1,8 +1,8 @@
 ################################################################################
 ### R script to compare several conditions with the SARTools and edgeR packages
 ### Hugo Varet
-### November 28th, 2019
-### designed to be executed with SARTools 1.7.4
+### March 23rd, 2022
+### designed to be executed with SARTools 1.8.1
 ### run "Rscript template_script_edgeR_CL.r --help" to get some help
 ################################################################################