To calculate differential expression in a proteomics dataset I would like to use LIMMA. I have two questions about it.
1) So far I did the LIMMA analysis on my log2 transformed data only, however it appears that it is recommended to perform a room transformation first as they represent counted data (from MS/MS DIA).
For the voom transformation a design matrix is requested, apparently the same one as for LIMMA. Due to batch effects I am blocking for two batches in the design matrix for LIMMA, using: design <- model.matrix(~0 + Causative.gene +batch_date.proc + batch_cell.number, data = pData(ExSet)).
Should I use the same matrix vor VOOM or better not include the batch block in it?
2) Could you tell me a code how I could check if VOOM transformation for my data is actually necessary? As much as I understand VOOM corrects for heteroskedacity in the data. How could I check if my expression data are actually affected by heteroskedacity? (I found this post about it but don't know how to apply this to my data with 170 samples and 8 groups to compare: https://datascienceplus.com/how-to-detect-heteroscedasticity-and-rectify-it/)
Thanks a lot! Sebastian