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)
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!