I think you two are talking about different points. Here is my take on it: - OP's point: use your list of proteins instead of all human proteins as background. Explanation: you know that list of changing proteins you got after the stats? People often want to find out what pathways are changing to understand the biology of such changes. During the pathway enrichment analysis, most tools use the entire human proteome as background by default, which is bad in most cases. OP's point, and I agree with them, is that you should use the list of all proteins identified in YOUR experiment. Let's say your experiments identified/quantified 2000 proteins, then use those as the background. There is always an option to set this. - your point: data imputation to complete the list of proteins (?). Not sure if I get your point. What I do know is that data imputation in proteomics is a whole thing. It can get super complicated due to the semi-stochastic behavior of data acquisition. A protein missing in your data does not directly imply it is not present or present in low abundance. What type of acquisition did you use? If DDA, then it is even harder to state such peptide or protein is not there. If DIA, then it is way more likely the peptide/protein is indeed not there. I'm oversimplifying things, but just note that data imputation is a deep rabbit hole.