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Old 06-05-2015, 02:13 AM   #1
dena.dinesh
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Default 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?
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Old 06-09-2015, 11:29 AM   #2
Michael Love
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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.
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Old 06-09-2015, 11:42 AM   #3
dena.dinesh
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Default

Quote:
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
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