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  • Finding the Similarity/dissimilarity of RNAseq samples using correlation

    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

    Code:
    ddsFullCountTable <- DESeqDataSetFromMatrix(countData = rnaseqMatrix,colData = conditions,design = ~ conditions)
    dds = DESeq(ddsFullCountTable)
    Later I transformed the dds to variance stabilizing transformation usig following command
    Code:
    vsd=varianceStabilizingTransformation(dds)
    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,

    Code:
    heatmap.2(cor(assay(vsd)), col = redgreen(75), key =TRUE, symkey= FALSE, density.info = "none", trace = "none", margins = c(10,10))
    Is my approach to use correlation matrix on vsd transformed data to find the samples similarity/dissimilarity is right?

  • #2
    You can calculate correlations, or if you take a look at the vignette, we also show how to calculate Euclidean distances of the VST data and perform hierarchical clustering.

    Comment


    • #3
      Originally posted by Michael Love View Post
      You can calculate correlations, or if you take a look at the vignette, we also show how to calculate Euclidean distances of the VST data and perform hierarchical clustering.
      Thanks for your reply. So if I take correlation on vst transformed data or calculate the the Euclidean distances of vst data and perform hierarchical clustering the resulting dendograms should be the same, isnt?

      Kindly guide me

      Comment

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