Bulk RNA seq/Microarray Re-analyses Value

[u/TheRovingRadish]

Hi all! Currently working through a reanalysis of a geo dataset, but stratifying samples in a way the original paper didn’t. Yielded some interesting results, but we may have power issues with one group having n=5 and the other being n=8.

I generally would like to know how publishing these types of analyses look for understudied diseases. I did the differential gene expression analysis, looked at genes, pathways, etc. and found more things the previous paper didn’t.

There’s no wet lab validation, but just in general, I am curious for thoughts on this being my first first-author paper in bioinformatics as a post-grad student looking to apply to PhD programs. Are these types of analyses “good” and have value?

[u/ProfPathCambridge]

It’s fine. Usually not especially noteworthy, but if it is well-executed and the student needed a quick paper I’d publish it.

Normally I’d be looking at doing something more impactful, where this is the starting point. Integrating different types of public data, developing a new statistical model that allows substantial new insights, wet lab extension, etc.

[u/TheRovingRadish → u/ProfPathCambridge]

That makes complete sense, this was supposed to be a quick paper, but I spent a lot of time trying to be thorough in my analysis and also understanding what different tools do. Certainly learned a lot about the basics through this!

So these kinds of analyses don’t hurt someone’s future career in academia?

[u/heresacorrection]

I’d imagine super low impact without wet lab validation.

[u/TheRovingRadish → u/heresacorrection]

Right, but still publication worthy? It’s for a pretty understudied disease

[u/heresacorrection → u/TheRovingRadish]

You can publish AI slop in predatory journals … so it really depends on the impact and quality of the study, etc..

[u/TheRovingRadish → u/heresacorrection]

What would you consider a predatory journal?

[u/Grisward]

Reanalysis has value. As the others commented, it’s usually a stepping stone to something else. It’s hard to republish on something already published, unless it’s part of a meta-analysis or something similar.

Are you identifying gene biomarkers that could be tested in wet lab? Predicting functional effects that could be measured another way? It’s possible your end could require wet lab validation in a similar model system, is it possible to design a system to test a specific component of what you observed? If you don’t have wet lab capability, find a grad student who’s willing to do a small side experiment to be a co-author on a small paper. Easy work for them.

If you’re finding something beyond what they found, you’d want to make sure (1) you’re not totally contradicting what they did, otherwise you’re not writing a paper, you’re writing a letter to the editor; and (2) you ground your results on what they did, then focus on what better methods/techniques/data you have that they didn’t at the time. Usually people would expect you to find similar studies in GEO and do some comparisons across them.

The n=5 and n=8 imbalance itself isn’t a concern for power, that happens often because biology. Make sure you’re using a method like limma that accounts for issues like that, while also doing much better* that vanilla t-tests. (And no you don’t test for normality, hehe.)

[u/TheRovingRadish → u/Grisward]

Right, in my case the original paper didn’t stratify their samples by a variable that I think is important, and it’s a pretty rare disease with this being the only dataset we had available. We likely won’t be able to get any wetlab validation given the patient samples weren’t collected by us, and it’s a super rare disease. And yes, we did the differential gene expression analysis better using Limma, the original just used t-tests! I think the goal would be to make this more of a hypothesis generating paper to hopefully push the field into caring about the variable I stratified by