Hi,
I am working on RNAseq data for 8 samples which biological duplicates. I want to find how close the samples are with respect to each other. So I obtained the count data and loaded the file into R. I loaded the library DESeq2 and later converted the count data into DeseqcounzTable and later into Dseq2 using following commands
Later I transformed the dds to variance stabilizing transformation usig following command
As I wanted to find the how similar/dissimilar the samples, I found the correlation matrix for the vsd transformed data and generated heatmap using following command,
Is my approach to use correlation matrix on vsd transformed data to find the samples similarity/dissimilarity is right?
I am working on RNAseq data for 8 samples which biological duplicates. I want to find how close the samples are with respect to each other. So I obtained the count data and loaded the file into R. I loaded the library DESeq2 and later converted the count data into DeseqcounzTable and later into Dseq2 using following commands
Code:
ddsFullCountTable <- DESeqDataSetFromMatrix(countData = rnaseqMatrix,colData = conditions,design = ~ conditions) dds = DESeq(ddsFullCountTable)
Code:
vsd=varianceStabilizingTransformation(dds)
Code:
heatmap.2(cor(assay(vsd)), col = redgreen(75), key =TRUE, symkey= FALSE, density.info = "none", trace = "none", margins = c(10,10))
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