Finding common pattern among heterogenous group of cancer

Question

[Hyper_Odin]

Hello all, I have RNA- seq data from different untreated cancer cell lines (obtained from different parts of the body). Some are responsive to treatment whereas others are not. I want to find a common pattern of genes or pathways that are modulated in both groups. I have already performed differential expression analysis and pathway analysis like GSEA, DAVID etc, and some common patterns have started to emerge. I would like to know if there are any tools or methodology that you would apply to these data sets to dig out interesting data?

Thank you

[Matthias Zepper]

It is just a quirk of mine, but I sometimes applied tools to data that were actually meant for a different purpose, if the inputs were sort of similar in structure as the same statistical methods might work neatly on that data as well. Of course, those findings require extra solid experimental proof afterwards to be publishable.

In your case, I am thinking that tools meant to correlate microbiome abundances with clinical outcome might highlight new patterns or interesting features to look at? You would feed in gene/transcript abundances as fake microbial abundances and your response to treatment as clinical pattern to correlate them to. Didn't try it, but maybe some new patterns emerge if you feed your data into MaAsLin2? Consult the paper first to see if it can really be applied to your problem.

Here be dragons...

A bit more to the point might be DysRegNet?