<p>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.</p>

CC BY-SA 3.0
2017-06-21T11:44:01.653Z
2017-06-21T11:44:01.653Z
Devon Ryan
<p>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). </p>

<p>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?</p>

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

<p>(this post was <a href="https://www.biostars.org/p/258889/" rel="noreferrer">crossposted on Biostars</a>)</p>

CC BY-SA 3.0
2017-06-21T11:33:45.887Z
Would a linear regression not make more sense as expression is not a binary outcome?
Kristoffer Vitting-Seerup
<rna-seq><r>
Correlating gene expression with qualitative variables
# Correlating gene expression with qualitative variables

(Asked by: username_0 on 2017-06-21)

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](https://www.biostars.org/p/258889/))

**Tags:** rna-seq, r
> **Comments:**
> **username_1**: Would a linear regression not make more sense as expression is not a binary outcome?  

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## 1 answers

### Answer by username_2 on 2017-06-21. Score: 3

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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