[BioC] Experimental design for RNA-Seq

[Naomi Altman]

At least from the stat theory point of view, the best design is equal 
numbers of biological samples (the more the better) for each 
condition and no technical reps.

So far, there is little indication that there are flowcell 
effects.  However, to be on the safe side, you should use the 
blocking principle - as much as possible distribute the reps from the 
different conditions across different flow cells (unless the whole 
experiment fits on a single flow cell).

--Naomi

At 04:02 AM 5/28/2010, michael watson (IAH-C) wrote:
>Dear List
>
>I'm about to design a simple experiment (knockout vs wild-type) and 
>we plan to use RNA-Seq.  We're interested in gene expression, for 
>mRNA and microRNAs in particular, and calculating stats for 
>differential expression.
>
>I'm aware of DEseq, DEGseq and edgeR.  I wanted to ask those who 
>have a lot of experience of this type of analysis if they have any 
>advice for experimental design, in particular, the number of 
>replicates they have used and why (I was planning on going for all 
>biological replicates, no technical).
>
>Thanks
>Mick
>
>
>
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>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111