Hello!

I have an inquiry about what package works best for my dataset since I find it a bit complex to understand.

I have a proteomics dataset; fish were exposed to 2 different drug treatments and a control, and each fish had its mucus sampled before exposure, 15 days after exposure, and 30 days after exposure (making it a repeated measures design).

Effectively, there is control for the treatment (0mg, 5mg, 10mg), and for the time (Day 0, Day 15 and Day 30).

Additionally, the proteomics data I have is in the form of peak intensities- where the rows are protein names, columns are the samples and the cells contain peak intensity values

What package works best with such a proteomics dataset, since a lot of them are designed mainly for transcriptomics data?

Any suggestions and explanations are welcome, since I would like to learn and apply this further!

Thank you:)

Contingent upon having no missing data, you can use limma. I usually exclude proteins with > 20% missing and then impute the rest, then use limma. An alternative if you don't want/can't impute is to use proDA.

Use the excellent tutorial on Proteomic analysis