[mcq.plot.peptide.intensity] modif pour que des trous apparaissent quand il y a des NA

MCQR/R/mcq_plot_peptide_intensity.R

@@ -77,24 +77,36 @@ mcq.plot.peptide.intensity<- function (object, flist=NULL, rCutoff = 0.5, nprot=
 
 		bad.cor=NULL
 		no.corr=NULL
+		tab.na=c(q=NA, combiFact=NA, peptiz=NA, protein=NA)
 		for (i in 1:length(protok)){
 			sub <- tab[tab$protein %in% protok[i],]
-			count=table(sub$peptiz)
-			no.corr=c(no.corr,names(count[count<5])) # the correlation is not computed for peptiz with less than 5 occurrences
-			sub=sub[!sub$peptiz %in% no.corr,]
-			matpep=tapply(sub$q,list(as.character(sub$peptiz), sub[,"combiFact"]),FUN=mean,na.rm=T)
+			count <- table(sub$peptiz)
+			no.corr <- c(no.corr,names(count[count<5])) # the correlation is not computed for peptiz with less than 5 occurrences
+			sub <- sub[!sub$peptiz %in% no.corr,]
+			matpep  <- tapply(sub$q,list(as.character(sub$peptiz), sub[,"combiFact"]),FUN=mean,na.rm=T)
+			temp <-stack(as.data.frame(matpep))
+			temp$peptiz <-rep(rownames(matpep), ncol(matpep))
+			temp$protein <- protok[i]
+			temp <- temp[which(is.na(temp$values)),]	
+			colnames(temp)[1:2]=c("q", "combiFact")
+			tab.na=rbind.data.frame(tab.na, temp)
 			ccc=cor(t(matpep),use="pairwise.complete.obs")
 			ccc=ccc*(col(ccc)!=row(ccc)) # sans la bissectrice
 			max.cor=apply(ccc,1,max, na.rm=TRUE)
 			bad.cor=c(bad.cor,names(max.cor[max.cor<rCutoff]))
 		}
 
-		sub <- tab[tab$protein %in% protok,]
+		tab.na <- tab.na[-1, ]
+		tab.na$imputed <- "not imputed"
+		sub <- tab[tab$protein %in% protok, ]
+		sub$imputed <-"not imputed"
+		sub$imputed[which(is.na(sub$nirank))]="imputed"	
+		sub <- sub[, c("q", "combiFact", "peptiz", "protein", "imputed")]
+		sub <- rbind.data.frame(sub, tab.na)	
+		
 		sub$bad.cor="correlated"
 		sub$bad.cor[sub$peptiz %in% bad.cor]="uncorrelated"
 		sub$bad.cor[sub$peptiz %in% no.corr]="not tested"
-		sub$imputed="not imputed"
-		sub$imputed[which(is.na(sub$nirank))]="imputed"
 
 		if (scale==TRUE){
 			mean.pep=ddply(sub, "peptiz", summarize, mean.int=mean(q))

new_example_data/script/lanceur_shotgun_strepto.R

@@ -93,7 +93,7 @@ Number of NA values : 180167
 	normalized_filtered_xic = mcq.drop.shared.peptides(normalized_filtered_xic)
 	mcq.plot.peptide.reproducibility(normalized_filtered_xic, file="./output/repro.pdf")
 	normalized_filtered_xic = mcq.drop.unreproducible.peptides(normalized_filtered_xic, method="percent", percentNA=0.1)
-	my_proteins= mcq.plot.peptide.intensity(normalized_filtered_xic, rCutoff=0.5, nprot=6, getProtlist=TRUE)
+	my_proteins= mcq.plot.peptide.intensity(normalized_filtered_xic, rCutoff=0.5, nprot=3, getProtlist=TRUE)
 	mcq.plot.peptide.intensity(normalized_filtered_xic, rCutoff=0.5, protlist=my_proteins, file="./output/pep_cor5.pdf")
 	mcq.plot.peptide.intensity(normalized_filtered_xic, rCutoff=0.3, protlist=my_proteins, file="./output/pep_cor3.pdf")
 	normalized_filtered_xic = mcq.drop.uncorrelated.peptides(normalized_filtered_xic, rCutoff=0.3)