I'd suggest taking logit(ratio) instead of log2(ratio) as the former is
quasi-normally-distributed (OK, logistic-normal) and the latter isn't.
 voom() would then presumably come up with the appropriate weights to deal
with the remaining inflation/deflation

WARNING: I don't use voom() on ratios, myself, so YMMV



On Tue, Jun 19, 2012 at 2:09 PM, Aaron Mackey <amackey@virginia.edu> wrote:

> There's a thread on the bioconductor mailing list about using voom for
> RSEM-based RNA-seq quantification, in which  Gordon Smythe explained that
> while voom() was designed for count data, it doesn't require it.  As Tim
> Triche has suggested, if you're raw data is really ratios (and not absolute
> values for normal and tumor), then you should take log2 of those ratios and
> use limma from there; you can then also hijack the arrayQualityMetrics
> package to check QC (MA plots, mean-variance relationships, etc.)
>
> -Aaron
>
> On Tue, Jun 19, 2012 at 3:39 PM, Yong Li <mail.yong.li@googlemail.com
> >wrote:
>
> > Dear Aaron,
> >
> > thank you for your quick answer! I have checked the help page of
> > voom() but it seems to be used for count data. My data are just
> > tumor/normal ratios. I am wondering if you could provide more details?
> >
> > Best regards,
> > Yong
> >
> > On Tue, Jun 19, 2012 at 8:18 PM, Aaron Mackey <amackey@virginia.edu>
> > wrote:
> > > yes, it should be possible with a voom()-based analysis to get the
> > variances
> > > "right".
> > >
> > > -Aaron
> > >
> > > On Tue, Jun 19, 2012 at 12:47 PM, Yong Li <mail.yong.li@googlemail.com
> >
> > > wrote:
> > >>
> > >> Hello,
> > >>
> > >> limma has been so valuable in microarray data analysis, but has anyone
> > >> used limma for finding differentially expressed proteins from
> > >> quantitative proteomics data? The data I got has tumor/normal ratios
> > >> of thousands proteins, and both tumor and normal have a number of
> > >> replicates. Could such data be analyzed with limma?
> > >>
> > >> If limma can not be used here, what statistics method is suitable so
> > >> that we can get statistically significant proteins with p-values? Any
> > >> suggestion is appreciated.
> > >>
> > >> Kind regards,
> > >> Yong
> > >>
> > >> _______________________________________________
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> > >> Bioconductor@r-project.org
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> > >
> > >
> >
> > _______________________________________________
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> >
>
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-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>

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