[BioC] Voom and CQN input

Gordon K Smyth smyth at wehi.EDU.AU
Wed Nov 27 08:48:18 CET 2013

Hi Heather,

> Date: Mon, 25 Nov 2013 16:53:02 -0800
> From: Heather Estrella <hestrella at regulusrx.com>
> To: "bioconductor at r-project.org" <bioconductor at r-project.org>
> Subject: [BioC] Voom and CQN input
> This is in regards to a previous post by Dr. Gordon Smyth from June, 
> 2012. https://stat.ethz.ch/pipermail/bioconductor/2012-June/045950.html
> In the previous post, Dr. Smyth stated: "voom() isn't currently setup to 
> accept the normalization matrix from cqn, but it will be down the 
> track."
> Does anyone know whether voom() can now accept CQN normalized output? I 
> was not able to find anything in the limma user documentation on whether 
> voom can now handle CQN normalized data as input.

No we have not implemented CQN style normalization in conjunction with 

In the meantime, you could try glmQLFTest() in edgeR which gives 
comparable results to voom and which does accept cqn normalization 

> I'm trying to correct for sample-specific variability prior to running 
> voom normalization and limma differential expression. I have been 
> reading publications on sample-specific technical variability (e.g. 
> Risso, et al.  BMC Bioinformatics 2011). Is it still recommended to do 
> within-lane quantile normalization prior to differential expression?

I don't find this necessary in my own RNA-seq analyses, which is why I 
have not been that motivated to implement it in voom.  I usually find TMM 
scale normalization sufficient.  If someone was to present me with a 
dataset for which it was clearly required, then that would be interesting.

Best wishes

> My data is RNA-Seq from Illumina HiSeq using standard Illumina 
> processing. I definitely have the random hexamer priming issue, but that 
> should (theoretically at least) be consistent across samples for a 
> single gene as Dr. Smyth points out in the previously referenced post.
> Any insight would be great.
> Kind regards,
> Heather

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