Hi,

I am currently analyzing a small RNAseq data with 10 biological replicates for each 3 different timepoints (time1, time2, time3) from a mouse data. Firstly I mapped and quantified the data with miRDeep2 and proceed the counts data with DESeq2.

As suggested I used varianceStabilizingTransformation(DESeq.ds, blind=FALSE) to transform the counts as an input for WGCNA. Strangely if I plot the PC1 of the module eigengenes for each modules predicted, I got an opposite results regarding the expression of each timepoint.

In my raw expression data as well as in my DEG list, I could see that genes that are expressed lower in time1 have a higher PC1 (pink dots) and genes that are expressed higher in time3 have a lower PC1 (blue dots)

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Can I interpret the PC1 value as an expression correlation of each time point? Or did I understood it all wrongly? I have rather only weak knowledge about statistics in general.

Thanks in advance for any kind of help and hints!

Dewi

I could not fully understand what you mean by strange or opposite results (your image link is broken). However, module eigengene (ME; PC1) indeed summarises a module's activity. Comparing raw expression values and ME is like apples and oranges, by the way. I would'nt be surprised that your ME showed a 'different' result simply because your gene of interest is somehow affected by other genes in the respective module.