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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)

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  • $\begingroup$ Would a linear regression not make more sense as expression is not a binary outcome? $\endgroup$
    – Kristoffer Vitting-Seerup
    Commented Jun 21, 2017 at 11:33

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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.

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