Merge pull request #71 from PF2-pasteur-fr/development

Development

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: SARTools
 Type: Package
 Title: Statistical Analysis of RNA-Seq Tools
-Version: 1.7.1
-Date: 2019-09-16
+Version: 1.7.2
+Date: 2020-01-13
 Author: Marie-Agnes Dillies and Hugo Varet
 Maintainer: Hugo Varet <[email protected]>
 Depends: R (>= 3.3.0),
@@ -33,4 +33,4 @@ VignetteBuilder: knitr, rmarkdown
 Encoding: latin1
 Description: Provide R tools and an environment for the statistical analysis of RNA-Seq projects: load and clean data, produce figures, perform statistical analysis/testing with DESeq2 or edgeR, export results and create final report.
 License: GPL-2
-RoxygenNote: 6.1.1
+RoxygenNote: 7.0.0

NEWS

@@ -1,6 +1,12 @@
+CHANGES IN VERSION 1.7.2
+------------------------
+	o the levels of the variable of interest are now ordered as they appear in the target file (after the reference level set as parameter)
+	o new default colors as some previous ones are not compatible with ggplot2
+
 CHANGES IN VERSION 1.7.1
 ------------------------
-        o remove RGraphics dependency as it causes problems with conda
+	o remove RGraphics dependency as it causes problems with conda
+	o replaced stat_summary_hex() by stat_summary_2d() in pairwiseScatterPlots() as it required to install the hexbin package
 
 CHANGES IN VERSION 1.7.0
 ------------------------

R/descriptionPlots.r

@@ -27,7 +27,7 @@ descriptionPlots <- function(counts, group, col=c("lightblue","orange","MediumVi
   # SERE and pairwise scatter plots
   cat("Matrix of SERE statistics:\n")
   print(tabSERE(counts))
-  pairwiseScatterPlots(counts=counts, group=group)
+  pairwiseScatterPlots(counts=counts)
 
   return(majSequences)
 }

R/diagSizeFactorsPlots.R

@@ -39,7 +39,7 @@ diagSizeFactorsPlots <- function(dds, group, col=c("lightblue","orange","MediumV
 
   # total read counts vs size factors
   if ("sf_libsize" %in% plots){
-    if (outfile) png(filename="figures/diagSizeFactorsTC.png", width=1900, height=1800, res=300)
+    if (outfile) png(filename="figures/diagSizeFactorsTC.png", width=2000, height=1800, res=300)
     d <- data.frame(sf=sizeFactors(dds), libsize=colSums(counts(dds))/1e6, 
                     group, sample=factor(colnames(dds), levels=colnames(dds)))
     print(ggplot(data=d, aes(x=.data$sf, y=.data$libsize, color=.data$group, label=.data$sample)) + 

R/dispersionsPlot.R

@@ -14,9 +14,9 @@ dispersionsPlot <- function(dds, outfile=TRUE){
   d <- as.data.frame(mcols(dds)[,c("baseMean", "dispGeneEst", "dispFit", "dispersion")])
   d <- d[which(d$baseMean > 0),]
   d <- data.frame(baseMean=rep(d$baseMean, 3),
-                  value=c(d$dispGeneEst, d$dispFit, d$dispersion),
-                  variable=factor(rep(c("dispGeneEst", "dispFit", "dispersion"), each=nrow(d)),
-                                  levels=c("dispGeneEst", "dispFit", "dispersion")))
+                  value=c(d$dispGeneEst, d$dispersion, d$dispFit),
+                  variable=factor(rep(c("dispGeneEst", "dispersion", "dispFit"), each=nrow(d)),
+                                  levels=c("dispGeneEst", "dispersion", "dispFit")))
   p1 <- ggplot(d, aes(x=.data$baseMean, y=.data$value, colour=.data$variable)) + 
     geom_point(size=0.1) +
     scale_x_continuous(trans = log10_trans(),
@@ -28,9 +28,9 @@ dispersionsPlot <- function(dds, outfile=TRUE){
     ylab("Dispersion") + 
     xlab("Mean of normalized counts") +
     scale_colour_manual(
-      values=c("Black", "#e41a1c", "#377eb8"), 
-      breaks=c("dispGeneEst", "dispFit", "dispersion"), 
-      labels=c("Estimate", "Fit", "Final"),
+      values=c("Black", "#377eb8", "#e41a1c"),
+      breaks=c("dispGeneEst", "dispersion", "dispFit"),
+      labels=c("Estimate", "Final", "Fit"),
       name="") +
     guides(colour = guide_legend(override.aes = list(size=2))) +
     ggtitle("Dispersions")

R/loadTargetFile.R

@@ -16,7 +16,9 @@ loadTargetFile <- function(targetFile, varInt, condRef, batch){
   if (!is.null(batch) && !I(batch %in% names(target))) stop(paste("The batch effect", batch, "is not in the target file")) 
   target[,varInt] <- as.factor(target[,varInt])
   if (!I(condRef %in% as.character(target[,varInt]))) stop(paste("The reference level", condRef, "is not a level of the factor of interest"))
-  target[,varInt] <- relevel(target[,varInt],ref=condRef)
+  lev <- c(condRef, unique(as.character(target[,varInt])))
+  lev <- lev[!duplicated(lev)]
+  target[,varInt] <- factor(target[,varInt], levels=lev)
   target <- target[order(target[,varInt]),]
   rownames(target) <- as.character(target[,1])
   # check if varInt contains replicates

R/pairwiseScatterPlots.R

@@ -3,12 +3,11 @@
 #' Scatter plots for pairwise comparaisons of log counts
 #'
 #' @param counts \code{matrix} of raw counts
-#' @param group factor vector of the condition from which each sample belongs
 #' @param outfile TRUE to export the figure in a png file
 #' @return A file named pairwiseScatter.png in the figures directory containing a pairwise scatter plot with the SERE statistics in the lower panel
 #' @author Marie-Agnes Dillies and Hugo Varet
 
-pairwiseScatterPlots <- function(counts, group, outfile=TRUE){
+pairwiseScatterPlots <- function(counts, outfile=TRUE){
   ncol <- ncol(counts)
   if (ncol <= 12){
     if (outfile) png(filename="figures/pairwiseScatter.png", width=cairoSizeWrapper(850*ncol), height=cairoSizeWrapper(700*ncol), res=300)
@@ -21,7 +20,7 @@ pairwiseScatterPlots <- function(counts, group, outfile=TRUE){
         if (i==j) next
         if (i > j){
           p[[k]] <- ggplot(data=cbind(d, z=1), aes_string(x=names(d)[i], y=names(d)[j], z="z")) +
-            stat_summary_hex(fun=function(z) log(sum(z)), bins=50, show.legend=FALSE) +
+            stat_summary_2d(fun=function(z) log(sum(z)), bins=60, show.legend=FALSE) +
             scale_x_continuous(trans = log10_trans(),
                                breaks = trans_breaks("log10", function(x) 10^x),
                                labels = trans_format("log10", math_format(~10^.x))) +

R/run.DESeq2.r

@@ -18,7 +18,7 @@
 
 run.DESeq2 <- function(counts, target, varInt, batch=NULL,
                        locfunc="median", fitType="parametric", pAdjustMethod="BH",
-		       cooksCutoff=TRUE, independentFiltering=TRUE, alpha=0.05, ...){
+                       cooksCutoff=TRUE, independentFiltering=TRUE, alpha=0.05, ...){
   # building dds object
   dds <- DESeqDataSetFromMatrix(countData=counts, colData=target, 
                                 design=formula(paste("~", ifelse(!is.null(batch), paste(batch,"+"), ""), varInt)))

README.md

@@ -35,7 +35,7 @@ Note: if you want to set a dedicated conda environment for SARTools, use `conda
 How to use SARTools?
 --------------------
 
-A HTML vignette is available within the vignettes folder on GitHub and provides extensive information on the use of SARTools. The user can also open it with `vignette("SARTools")` if it has been generated during the installation of the package.
+A HTML vignette is available within the vignettes folder on GitHub and provides extensive information on the use of SARTools. The user can also open it with `vignette("SARTools")` if it has been generated during the installation of the package. Note that it is not available when SARTools has been installed using conda.
 
 Be careful to use the R script associated with the version of SARTools installed on your system.