# What are the ways to process a list of differentially expressed genes?

We are studying six different human macrophage/dendritic cell types isolated from healthy skin. They all differ from each other in a few cell surface markers.

We are interested in the characteristics of each cell type ("marker" genes or biological processes), the differences between them (especially cell surface proteins), and their relations (e.g. "which are the closest relatives?") as can be inferred from the transcriptome, from an immunological point of view. The wider context is HIV-infection, thus HIV infection-related differences (and similarities) are of particular interest.

One naive approach is to contrast two of the most similar cell types (as inferred from previous knowledge, the sorting strategy and the PCA plot), and produce a list of differentially expressed (DE) genes. I have now a list of ~500 DE genes between two cell types, together with fold changes, expression means etc., produced with DESeq2 on bulk RNA-seq data.

What are the best practices to process this list to answer some of the above?

I've found PPI analysis on NetworkAnalyst, and GO and pathway analysis on InnateDB useful.

What other options are available?

I'm particularly interested in approaches using R.

• The question you should be asking isn't "what can we do with this?", but rather "what biological question do we want to answer?" You sound like you're aimlessly fishing for anything, which is likely not worth your time. – Devon Ryan Jun 8 '17 at 12:11
• What do you mean by "process"? Process the list in order to do what? What question are you asking? Please edit your post and clarify what you are trying to do. As it stands, a valid answer would be that you can use wc to count the number of lines in the file. That is a type of "processing" but, obviously, not very helpful for you. The more specific you make your question, the higher the chances of your getting a useful answer. – terdon Jun 8 '17 at 12:11
• Hi Peter, thanks for your question and welcome to Bioinformatics Stack Exchange. Bioinformatics is a very large subject area, frequently with many different solutions to the same problem. It would be helpful if you could give more story / context surrounding your problem to guide answers. For example, what organism are you working with? Are these conditions treatment vs control, or separate treatments (without a control)? What kind of results are you interested in getting out of this analysis? – gringer Jun 8 '17 at 12:13
• Thank you all for the feedback. I understand that each dataset has to be studied according to the questions of interest, just wanted to get a few ideas. Also tried to make the question general so that it will be useful for more people, but I now added the specifics. – Peter Jun 8 '17 at 12:53
• I think the question is still too broad. What knowledge do you expect to learn? Could you narrow more to one of your interests (Process, pathways, or relations between cells lines)? Which is your "immunological point of view" ? – llrs Jun 8 '17 at 14:33