I'm hoping to use limma to analyze a metabolite dataset for differential 'expression' across replicate conditions (~200 metabolites).
I've normalized and log-transformed my data with MetaboAnalyst defaults (normalize metabolites to the sum for a sample -> log10 transform -> row-scale metabolites across samples).
I have a random-effects design and I like limma's ability to account for this... And I have seen some mass spec analyses advocating limma for similar analyses, although this helpful post seems to advocate against it, although I'm not as convinced given limma's widespread use on RNAseq (with voom).
Is there any reason I shouldn't do this? It seems to work well, but I'm a bit concerned that the default row-scaling masks mean-variance relationships (which maybe would be better accounted for with voom on the raw data)?
Thanks for any advice!