[BioC] heatmap with variance stabilizing transformed expression data in DESeq

[Wang, Li]

Dear List Members

I am a bit confused with the function of varianceStabilizingTransformation in DESeq. 

I used the function to transform my expression data of above four fold change differentially expressed genes, and applied heatmap.2 of gplots packages to generate the heatmap of transformed data. I found that the expressional difference between my two conditions after transformation turned to be smaller. In the manual of DESeq, the example figure about heatmap of transformed data also shows less color difference between two conditions. 

However, that is opposite to my purpose.  Could anyone give me some suggestion what kind of transformation of data I should do to show the expressional difference of two conditions?

My command used is as follows:
library(DESeq)
library(gplots)
count <- read.delim("500FourFCgeneWithExpression.txt", header=T, row.names=1)
design <- read.delim("design.csv", header=T, row.names=1)
head(design)
condition <- design$condition
condition
cds <- newCountDataSet(count, condition)
cds
cds <- estimateSizeFactors(cds)
sizeFactors(cds)
cds_blind <- estimateDispersions(cds, method="blind", fitType="local")
vsd <- varianceStabilizingTransformation(cds_blind)
pdf("heatmap20130508_DESeq.pdf")
heatmap.2(exprs(vsd), Rowv=TRUE, Colv=NA, dendrogram="row", col=redgreen(75), trace="none", margin=c(10,6))
dev.off()

I will appreciate for any suggestions. 

Cheers
Li