ggplot3.3.0

DESCRIPTION

@@ -2,10 +2,10 @@ Package: ChIPuanaR
 Type: Package
 Title: Differential analysis of ChIP-Seq data
 Version: 0.99.0
-Date: 2020-04-06
+Date: 2020-05-07
 Author: Marie-Agnes Dillies, Hugo Varet and Maëlle Daunesse
 Maintainer: Maëlle Daunesse <maelle.daunesse@pasteur.fr>
-Depends: R (>= 3.4.0), DESeq2, limma, kableExtra, knitr, ggplot2
+Depends: R (>= 3.4.0), DESeq2, limma, kableExtra, knitr, ggplot2 (>= 3.3.0)
 Imports: stats, utils, graphics, ggrepel, grDevices, grid, ggdendro, gridExtra, rmarkdown (>= 1.4), SummarizedExperiment, S4Vectors, scales
 biocViews: Software
 VignetteBuilder: knitr
@@ -15,4 +15,4 @@ Description: Provide R tools and an environment for the differential
 	perform statistical analysis/testing with DESeq2 or limma, export
 	results and create final report.
 License: GPL-2
-RoxygenNote: 7.0.0
+RoxygenNote: 7.1.0

R/MAPlot.R

@@ -33,7 +33,7 @@ MAPlot <- function(results, method, alpha=0.05, outfile=FALSE, log2FClim=NULL){
       scale_colour_manual(values=c("no"="black", "yes"="red"), drop=FALSE) +
       scale_shape_manual(values=c("bottom"=25, "in"=21, "top"=24), drop=FALSE) +
       scale_fill_manual(values=c("no"="black", "yes"="red"), drop=FALSE) +
-      scale_y_continuous(expand=expand_scale(mult=c(0.03, 0.03))) +
+      scale_y_continuous(expand=expansion(mult=c(0.03, 0.03))) +
       ylab(expression(log[2]~fold~change)) +
       ggtitle(paste0("MA-plot - ", gsub("_"," ",name)))
     if (method=="DESeq2"){

R/barplotTotal.R

@@ -21,7 +21,7 @@ barplotTotal <- function(counts, conditions,
           scale_fill_manual(values=col) +
           xlab("Samples") + 
           ylab("Total read count (million)") +
-          scale_y_continuous(expand=expand_scale(mult=c(0.01, 0.05))) +
+          scale_y_continuous(expand=expansion(mult=c(0.01, 0.05))) +
           ggtitle("Total read count per sample (million)") +
           theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5)))
   if (outfile) dev.off()

R/clusterPlot.R

@@ -18,6 +18,6 @@ clusterPlot <- function(counts.trans, conditions, outfile=FALSE){
           ggtitle("Cluster dendrogram\nEuclidean distance, Ward criterion") +
           theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),
                 axis.text.y=element_text(angle=0)) +
-          scale_y_continuous(expand=expand_scale(mult=c(0.01, 0.05))))
+          scale_y_continuous(expand=expansion(mult=c(0.01, 0.05))))
   if (outfile) dev.off()
 }

R/rawpHist.R

@@ -17,7 +17,7 @@ rawpHist <- function(result, outfile=FALSE){
     result.name <- result[[name]]
     p[[name]] <- ggplot(data=result.name, aes(x=.data$pvalue)) +
       geom_histogram(color="white", breaks=seq(0, 1, by=0.025)) +
-      scale_y_continuous(expand=expand_scale(mult=c(0.01, 0.05))) +
+      scale_y_continuous(expand=expansion(mult=c(0.01, 0.05))) +
       xlab("Raw p-value") +
       ylab("Frequency") +
       ggtitle(paste0("Distribution of raw p-values - ", gsub("_"," ",name)))

README.md

@@ -13,5 +13,4 @@ To install the ChIPuanaR package from GitLab, open a R session and:
 	- Some users may have to install the pandoc and pandoc-citeproc libraries to be able to generate the final HTML reports
 
 - For Windows users only, install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) or check that it is already installed (needed to build the package)
-- Load the devtools R package with `library(devtools)`
-- Run `install_gitlab(repo="hub/chipuanar", host="gitlab.pasteur.fr", build_vignettes=TRUE)`
+- Run `devtools::install_gitlab(repo="hub/chipuanar", host="gitlab.pasteur.fr", build_vignettes=TRUE)`

inst/Report_ChIPuanaR.Rmd

@@ -47,6 +47,7 @@ if (method=="Limma") {
     colnames(sf) <- "Size factor"
   }
   counts.trans <- resAnDif$voom$E
+  colnames(counts.trans) <- gsub("^norm.", "", colnames(counts.trans))
 ############### DESeq2 ########################################
 } else if(method=="DESeq2") {
   library(DESeq2)
@@ -226,21 +227,15 @@ A p-value adjustment is performed to take into account multiple testing and cont
 ```{r echo=FALSE, results="asis"}
 df <- matrix(NA, ncol = 3, nrow = length(resAnDif$results),
              dimnames=list(names(resAnDif$result), c("Total peaks", "Peaks up", "Peaks down")))
-
 for (name in names(resAnDif$results)) {
  results.name <- resAnDif$results[[name]]
- # Total number of peaks
- peaks <- as.integer(nrow(results.name[which(results.name$padj<=alpha),]))
- #Nb gene up
- peaks.up <- as.integer(nrow(results.name[which(results.name$padj<=alpha & results.name$log2FoldChange>0),]))
- #Nb gene down
- peaks.down <-as.integer(nrow(results.name[which(results.name$padj<=alpha & results.name$log2FoldChange<0),]))
- df[name,] <- c(peaks, peaks.up, peaks.down)
+ peaks.up <- nrow(results.name[which(results.name$padj <= alpha & results.name$log2FoldChange > 0),])
+ peaks.down <- nrow(results.name[which(results.name$padj <= alpha & results.name$log2FoldChange < 0),])
+ df[name,] <- c(peaks.up+peaks.down, peaks.up, peaks.down)
 }
-
 kable(df, caption = "Table 4: Normalization factors",format = "html") %>%
   kable_styling(c("striped", "bordered", "responsive"), font_size = 14) %>%
-  column_spec(1, bold = T)
+  column_spec(1, bold = TRUE)
 ```
 
 Figure 8 represents the MA-plot of the data for the comparisons done, where differentially expressed features are highlighted in red. A MA-plot represents the log ratio of differential expression as a function of the mean intensity for each feature. Triangles correspond to features having a too low/high $\log_2(\text{FC})$ to be displayed on the plot.