Hello awesome community!
I just wanted to know your expert opinion, whether
limma-voom protocol is suitable for analysing ChIPseq data(with
macs2 peaks and raw read counts generated from
featureCounts). Does it assume that majority of the expression will not change among groups, just like
DESeq2? I am having trouble with my ChIPseq data analysis as I am expecting global change of histone modification, which doesn't follow
DiffBind seemed to be working to some extent to capture the global change with its simple BAM file library size normalization, but my experimental design is too complex to block multiple unwanted factors and get proper differential binding regions.... I was thinking of bypassing
sizeFactor normalization and feed simple BAM library size factors(just like
DiffBind) and then doing
nbinomWaldTest functions to perform differential analysis. I know in this case, running a spike-in control would have been optimal, but it is not possible right now.
I would really appreciate if you can comment on which sets of tool should be optimal to capture global histone modification differences when spike in control is not available.