Fix to circumvent plotting crashing on certain low-variance datasets

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: NormalyzerDE
 Title: Evaluation of normalization methods and calculation of differential expression analysis statistics
-Version: 1.3.3
+Version: 1.3.4
 Maintainer: Jakob Willforss <[email protected]>
 Author: Jakob Willforss
 Authors@R: c(

NEWS

@@ -1,20 +1,32 @@
+CHANGES IN VERSION 1.3.4
+----------------------------------
+
+* Certain datasets with fixed imputed values caused large difference in CV and MAD
+  for RLR normalization, which in turn lead to a crash in the plotting functions.
+  This update makes sure that the plotting can be carried out despite extreme values.
+
 CHANGES IN VERSION 1.3.3
+----------------------------------
 
 * Extended input validation when executing NormalyzerDE using experimentObj
 
 CHANGES IN VERSION 1.3.2
+----------------------------------
 
 * Correctly showing the number of values replaced with NA in status text
 
 CHANGES IN VERSION 1.3.0
+----------------------------------
 
 * Sync with Bioconductor changes
 
 CHANGES IN VERSION 1.1.16
+----------------------------------
 
 * Clearer input verification error for both incorrect fields and when values < 1 are encountered
 
 CHANGES IN VERSION 1.1.14/15
+----------------------------------
 
 * Updating tests to avoid cyclic dependency on the NormalyzerDE package
 

R/generatePlots.R

@@ -473,6 +473,8 @@ plotReplicateVariance <- function(nr, currentLayout, pageno) {
 #' @keywords internal
 plotReplicateVarAndStableVariables <- function(nr, currentLayout, pageno) {
 
+    # browser()
+
     nds <- nds(nr)
     methodnames <- names(normalizations(nr))
     currentjob <- jobName(nds)
@@ -483,6 +485,9 @@ plotReplicateVarAndStableVariables <- function(nr, currentLayout, pageno) {
     avgmadmempdiff <- avgmadmempdiff(ner)
     avgvarmempdiff <- avgvarmempdiff(ner)
 
+    lowBound <- min(c(avgcvmempdiff, avgmadmempdiff, avgvarmempdiff)) - 10
+    highBound <- max(c(avgcvmempdiff, avgmadmempdiff, avgvarmempdiff)) + 5
+
     tout <- rbind(c(1, 2, 3), c(4, 5, 5))
     graphics::layout(tout)
     graphics::par(mar=c(6, 6, 3, 1), oma=c(2, 3, 3, 2), xpd=NA)
@@ -492,8 +497,9 @@ plotReplicateVarAndStableVariables <- function(nr, currentLayout, pageno) {
         main="PCV compared to log2 ", 
         names.arg=c(methodnames), 
         border="red", 
-        ylim=c(min(avgcvmempdiff) - 10, 
-               max(avgmadmempdiff) + 5), 
+        ylim=c(lowBound, highBound),
+        # ylim=c(min(avgcvmempdiff) - 10, 
+        #        max(avgmadmempdiff) + 5), 
         density=20, 
         cex=0.9, 
         cex.axis=0.7, 
@@ -511,8 +517,9 @@ plotReplicateVarAndStableVariables <- function(nr, currentLayout, pageno) {
         main="PMAD compared to log2", 
         names.arg=c(methodnames), 
         border="red", 
-        ylim=c(min(avgmadmempdiff) - 10, 
-               max(avgmadmempdiff) + 5), 
+        ylim=c(lowBound, highBound),
+        # ylim=c(min(avgmadmempdiff) - 10, 
+        #        max(avgmadmempdiff) + 5), 
         density=20, 
         cex=0.9, 
         cex.axis=0.7, 
@@ -530,13 +537,15 @@ plotReplicateVarAndStableVariables <- function(nr, currentLayout, pageno) {
         las=2
     )
 
+    # browser()
     abc <- graphics::barplot(
         avgvarmempdiff, 
         main="%PEV - compared to log2", 
         names.arg=c(methodnames), 
         border="red", 
-        ylim=c(min(avgvarmempdiff) - 10, 
-               max(avgmadmempdiff) + 5),
+        ylim=c(lowBound, highBound),
+        # ylim=c(min(avgvarmempdiff) - 10, 
+        #        max(avgmadmempdiff) + 5),
         density=20, 
         cex=0.9, 
         cex.axis=0.7,