Hi Brian,

You could also use svaseq in the devel branch of sva. It gives very similar
results to ruvseq. Here are some analysis files with examples:

http://jtleek.com/svaseq/simulateData.html

http://jtleek.com/svaseq/zebrafish.html

http://jtleek.com/svaseq/geuvadis.html

As Gordon mentioned, you should be careful about doing downstream
differential expression analysis on "batch cleaned" data - since the
degrees of freedom will not be correct you often end up with overly
optimistic inference. There isn't currently a good statistical method for
dealing with post batch removed data in differential expression or
clustering analyses.

Best,

Jeff


On Mon, Jul 21, 2014 at 12:36 PM, davide risso <risso.davide@gmail.com>
wrote:

> Hi Brian,
>
> as mentioned by Dario the RUVSeq package has a way to deal with your
> situation.
> The typical application of RUVSeq is differential expression, hence
> "adding" the batch effect in the generalized linear model rather than
> correcting the counts for the batch effects.
>
> However, the RUVSeq functions return a matrix of normalized counts
> that are "batch effect free". The main risk is that you remove (part
> of) the signal of interest when removing the batch effects (especially
> if the two are correlated). On the other hand, if the batch effect and
> the signal of interest are "not too correlated" RUVSeq will give you
> exactly what you want.
>
> If you have replicate samples, we found that in practice the
> "replicate method" (function RUVs) works much better than the
> "negative controls" method (function RUVg) when dealing with
> unsupervised problems.
>
> I hope this helps.
>
> Best,
> davide
>
> On Sat, Jul 19, 2014 at 8:01 PM, Brian Haas <bhaas@broadinstitute.org>
> wrote:
> > Greetings all,
> >
> > I've been researching ways to remove batch effects from RNA-Seq count
> > matrices.  Basically, I'm starting with a counts matrix that includes
> batch
> > effects, and want to generate a new matrix of counts that has the batch
> > effects removed.
> >
> > I'm looking to apply this to sets of RNA-Seq samples (~100 samples) that
> > were sequenced in batches on different days (factor) and for which I also
> > have other metadata with continuous values (covariates such as total
> > sequenced reads in each sample, quality metrics, etc).   I want to study
> > all these samples in an unsupervised manner, and don't have a model for
> > anything but the various batch effects that I want removed (ie. no cancer
> > vs. normal labeling, instead they're all 'normal' and I'd like to see if
> > they form clusters based on natural variation in the population, and
> > perhaps identify subtypes).
> >
> > >From what I've read thus far, methods like sva (and the included Combat)
> > require that you provide a model for the covariates that you do not want
> > removed (biological factors) in addition to the ones you do want removed
> > (batch effects).   Is it not possible to use these methods in my
> scenario,
> > where I don't have factors other than the specified batch effects?
> >
> > In searching the bioconductor mailing list archive, I found:
> >
> >        edgeR package, removeBatchEffect() function
> >
> > which seems to do exactly what I want, and I'll experiment with it
> shortly.
> >  I'm mostly curious about what other methods might be available to do
> this,
> > and whether the SVA or other libraries contain functions that I should
> > explore.
> >
> > Many thanks in advance for any advice!
> >
> > ~brian
> >
> >         [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > Search the archives:
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>
>
>
> --
> Davide Risso, PhD
> Post Doctoral Scholar
> Department of Statistics
> University of California, Berkeley
> 344 Li Ka Shing Center, #3370
> Berkeley, CA 94720-3370
> E-mail: davide.risso@berkeley.edu
>
> _______________________________________________
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