I have a gene expression dataset that I want to investigate. Particularly, I would like to understand whether there is any correlation between each gene's expression and some quantitative or qualtitative data (say, correlation between gene 'XPTO' , body mass index, and race).

One possible way to test this would be through logistic regression, but is this a good approach or are there caveats that I should know about using such a statistic?

My question is the following: which methods would you advise to measure such correlations, and why?

(this post was crossposted on Biostars)

  • $\begingroup$ Would a linear regression not make more sense as expression is not a binary outcome? $\endgroup$ – Kristoffer Vitting-Seerup Jun 21 '17 at 11:33

Logistic regression would generally be a bad choice for non-binary outcomes. In such cases, linear regression (or a GLM more generally) still works fine. You can already do that in the standard R packages for RNAseq (DESeq2, edgeR, and limma), where the fold-change is in whatever units you're measuring your quantitative trait in.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.