[BioC] Repeated Measures mRNA expression analysis

Gordon K Smyth smyth at wehi.EDU.AU
Wed Jun 26 01:57:13 CEST 2013

Dear Charles,

The term "repeated measures" describes a situation in which repeated 
measurements are made on the same biological unit.  Hence the repeated 
measurements are correlated.  It is not clear from the brief information 
you give whether this is the case, or whether the different time points 
derive from independent biological samples.

The model you give might or might not be correct, depending on the 
experimental units and the hypotheses that you plan to test.  For most 
experiments it is not the right approach, for reasons that I have pointed 
out elsewhere:


Best wishes

> Date: Mon, 24 Jun 2013 15:08:48 -0500
> From: Charles Determan Jr <deter088 at umn.edu>
> To: bioconductor at r-project.org
> Subject: [BioC] Repeated Measures mRNA expression analysis
> Greetings,
> I need to analyze data collected from an RNA-seq experiment.  This 
> consists of comparing two groups (control vs. treatment) and repeated 
> sampling (1 hour, 2 hours, 3 hours).  If this were a univariate problem 
> I know I would use a 2-way rmANOVA analysis but this is RNA-seq and I 
> have thousands of variables.  I am very familiar with multiple packages 
> for RNA differential expression analysis (e.g. DESeq2, edgeR, limma, 
> etc.) but I have been unable to figure out what the most appropriate way 
> to analyze such data in this circumstance.  The closest answer I can 
> find within the DESeq2 and edgeR manuals (limma is somewhat confusing to 
> me) is to place to main treatment of interest at the end of the design 
> formula, for example:
> design(dds) <- formula(~ time + treatment)
> Is this what is considered the appropriate way to address repeated measures
> in mRNA expression experiments?  Any thoughts are appreciated.
> Regards,
> -- 
> Charles Determan
> Integrated Biosciences PhD Candidate
> University of Minnesota

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