Unsupervised heatmap use tpm or rlog gave different results
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@jarod_v6liberoit-6654
Last seen 2.6 years ago
Italy

I interested on unsupervised clustering of my samples. I have made differential expression analysis using deseq2 after import data as tximport vignette from rsem.

I try to compare two heatmap starting from counting  counts or rlog. I found two different results. Is it normal?

norm.counts <- counts(dds, normalized=TRUE)
log.norm.counts <- log2(norm.counts + 1)

topVarGenes <- order(-rowVars(log.norm.counts)[0:1000])

mat<-log.norm.counts[topVarGenes,]
mat<-mat -rowMeans(mat)

pheatmap(mat,method="complete",main = " ",color=my_pal2, show_rownames = F,
         annotation_legend = FALSE, legend=T, cluster_cols=TRUE,cexRow=0.55,
         cluster_rows = T,breaks = quantile(mat,seq(0,1,length.out = length(my_pal2)+1)))

topVarGenes1 <- order(-rowVars(assay(rld)))[0:1000]
mat1 <- assay(rld)[ topVarGenes1, ]
mat1<- mat1 - rowMeans(mat1)

pheatmap(mat1,method="complete",main = "Unsupervised 1000 genes ",color=my_pal2, show_rownames = F,annotation_legend = FALSE, legend=T, cluster_cols=TRUE,breaks = quantile(mat1,seq(0,1,length.out = length(my_pal2)+1))

The results are different. What is the error here?

 

 

deseq2 tpm heatmap pheatmap • 1.4k views
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@mikelove
Last seen 2 hours ago
United States

That they are different is a result described in the DESeq2 paper, and there we show from simulations that rlog gave better performance in clustering compared to log2(normalized count + 1).

Here's the DESeq2 paper:

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8

You can see Fig 5 for a qualitative comparison and Figure S17 for the simulation results.

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