Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-06-21T11:44:01.653Z
Answers.LastActivityDate: 2017-06-21T11:44:01.653Z
Answers.UserDisplayName: Devon Ryan
Body: <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>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-06-21T11:33:45.887Z
Comments.Text: Would a linear regression not make more sense as expression is not a binary outcome?
Comments.UserDisplayName: Kristoffer Vitting-Seerup
Tags: <rna-seq><r>
Title: Correlating gene expression with qualitative variables
ThreadText: # 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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