fixedBugs

R/countsBoxplots.R

@@ -5,7 +5,7 @@
 #' @name countsBoxplots
 #' @param results results produced by DESeq2 or limma with ChIPuana
 #' @param conditions factor vector of the condition from which each sample belongs
-#' @param method either "DEseq2" or "Limma"
+#' @param method either "DESeq2" or "Limma"
 #' @param col colors of the boxplots (one per biological condition)
 #' @param outfile TRUE to export the figure in a png file
 #' @return A boxplots of the raw and normalized counts
@@ -38,14 +38,14 @@ countsBoxplots <- function(results, conditions, method,
     theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5))
   
   # norm counts
-  if(method=="DEseq2") {
+  if(method=="DESeq2") {
     norm.count <- log2(norm.counts+1)
   }
   norm.count <- removeNull(norm.counts)
   d <- stack(as.data.frame(norm.counts))
   d$conditions <- rep(conditions, each=nrow(norm.counts))
   p2 <- ggplot(d) + 
-    geom_boxplot(aes(x=.data$ind, y=.data$values+1, fill=.data$conditions), show.legend=TRUE) +
+    geom_boxplot(aes(x=gsub("norm.", "", .data$ind), y=.data$values+1, fill=.data$conditions), show.legend=TRUE) +
     labs(fill="") +
     scale_y_continuous(trans = log10_trans(),
                        breaks = trans_breaks("log10", function(x) 10^x),

R/run.DESeq2.r

@@ -49,6 +49,6 @@ run.DESeq2 <- function(counts, conditions, batch=NULL, pAdjustMethod="BH", spike
     results[[paste0(levelTest,"_vs_",levelRef)]] <- resAnDif
     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), counts.trans=assay(vst(dds))))
 }
 

inst/Report_ChIPuanaR.Rmd

@@ -29,25 +29,27 @@ counts <- as.matrix(data[,-c(1:6)])
 rownames(counts) <- data[,1]
 ```
 
-```{r echo=FALSE, results="asis", results="hide"}
+```{r echo=FALSE, results="asis", results="hide", message=FALSE}
 ############### LIMMA ########################################
 if (method=="Limma") {
   library(limma)
   if (is.null(spikes)){
     # Diferential analysis
     resAnDif <- run.Limma(counts = counts, conditions = Conditions,
-                          normalize.method = normalisation, pAdjustMethod = pAdjustMethod, outfile = FALSE)
+                          normalize.method = normalisation, 
+                          batch=batch, pAdjustMethod = pAdjustMethod, outfile = FALSE)
   } else {
     # Diferential analysis
     resAnDif <- run.Limma(counts = counts, conditions = Conditions, 
-                          normalize.method = "none", pAdjustMethod = pAdjustMethod, spikes = spikes, outfile = FALSE)
+                          normalize.method = "none", pAdjustMethod = pAdjustMethod, 
+                          batch = batch, spikes = spikes, outfile = FALSE)
     # Normalisation factor
     sf <- as.data.frame(spikes/max(spikes))
     colnames(sf) <- "Size factor"
   }
-  
+  counts.trans <- resAnDif$voom$E
 ############### DESeq2 ########################################
-} else if(method=="DEseq2") {
+} else if(method=="DESeq2") {
   library(DESeq2)
     if (is.null(spikes)){
       normalisation = "geometrical"
@@ -65,6 +67,7 @@ if (method=="Limma") {
       sf <- as.data.frame(spikes/max(spikes))
       colnames(sf) <- "Size factor"
     }
+  counts.trans <- resAnDif$counts.trans
 }
 
 # Export result table
@@ -135,14 +138,14 @@ Figure 3 sample clustering based on normalized data. An euclidean distance is co
 
 ```{r clusterplot, echo=FALSE,fig.align="center",fig.cap="Figure 3: Sample clustering based on normalized data.", out.width="600px", warning=FALSE, message=FALSE}
 # Cluster plot
-clusterPlot(counts.trans = resAnDif$voom$E,conditions = Conditions, outfile = FALSE)
+clusterPlot(counts.trans = counts.trans,conditions = Conditions, outfile = FALSE)
 ```
 
 Another way of visualizing the experiment variability is to look at the first principal components of the PCA, as shown on the figure 4. On this figure, the first principal component (PC1) is expected to separate samples from the different biological conditions, meaning that the biological variability is the main source of variance in the data.
 
 ```{r PCA, echo=FALSE,fig.align="center",fig.cap="Figure 4: First three components of a Principal Component Analysis, with percentages of variance associated with each axis.", out.width="1200px"}
 # PCA plot
-PCAPlot(counts.trans = resAnDif$voom$E, conditions = Conditions, outfile = FALSE)
+PCAPlot(counts.trans = counts.trans, conditions = Conditions, outfile = FALSE)
 ```
 
 

inst/config.R

 file <- "K4Me3_MOUSE_CuU_Peak.mtx"
 Conditions <- c("C","C","U","U")
 Replicates <- c("Rep1", "Rep2", "Rep1", "Rep2")
-method <- "Limma" #Limma
+method <- "DESeq2" # Limma or DESeq2
 normalisation <- "scale" # "scale" 
 spikes <- NULL
 pAdjustMethod <- "BH"

man/countsBoxplots.Rd

@@ -18,7 +18,7 @@ countsBoxplots(
 
 \item{conditions}{factor vector of the condition from which each sample belongs}
 
-\item{method}{either "DEseq2" or "Limma"}
+\item{method}{either "DESeq2" or "Limma"}
 
 \item{col}{colors of the boxplots (one per biological condition)}