Thank you for your comment! Basically, DESeq2 would not improve analysis from the biological point of view that much, improvement may be visible in those samples that have large deviation caused by measuring error only...
To obtain biological meaning of datasets, I use GSEA which is based on Kolmogorov–Smirnov algorithm, and then I analyse biological pathways and genes within them.
For proteomics data, size is not even close as of RNA-seq - you get your results in one Excel file :)
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That makes sense. I arrived at DESeq2 through examining microbial population changes and it's been super helpful in that context. It's always fun to see how a tool of choice can vary in efficacy between problems.