I'm new to proteomics data and my experience with it is quite different from transcriptomics data (RNA-Seq). I got some protein abundance data of samples from control group and exposure group (in vivo experiment exposing fish to toxicant, 10 samples in each group). The purpose is to find out the differentially expressed proteins. I used the package DEP to do the preprocessing and statistical analysis. In the end, the
adjusted P values (p-adj) are very high (either equals 1 or close to 1) and the
fold change is also almost 1. So if I use
p-adj then no protein is differentially expressed. I read some papers about proteomics data (the ones citing that package) and they report to get very low
p-adj (0.05 is used as threshold). I suspect that I didn't do the analysis in the correct way but I couldn't find out the problem...
The steps I've done to the raw abundance data are: remove the proteins who have more than half missing values in any group, transform the raw abundance with
arcsin, impute the missing values using maximum likelihood estimation, use the function
test_diff from DEP to do differential analysis.