[BioC] Voom and CQN input

[Gordon K Smyth]

Maybe that could be a good enough approximation, but in principle the 
adjustment should be made internally in voom so that voom can figure out 
the true size of each count, while also preserving the normalization 
factors, as it currently does with the library sizes.

Gordon

  > Date: Mon, 25 Nov 2013 23:01:39 -0800
> From: Ryan <rct at thompsonclan.org>
> To: Heather Estrella <hestrella at regulusrx.com>
> Cc: "bioconductor at r-project.org" <bioconductor at r-project.org>
> Subject: Re: [BioC] Voom and CQN input
>
> Given that voom is not restricted to integer values, is there any
> reason one couldn't simply apply the normalization manually before
> calling voom on the data?
>
> On Mon Nov 25 16:53:02 2013, Heather Estrella wrote:
>> 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. 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? 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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