Entering edit mode
Dear Ina,
voom is already quite robust as it is (more so than un-robustified
edgeR).
However limma also has special options for either observation or
dispersion outliers.
In the sequence
v <- voom(y,design)
fit <- lmFit(v,design)
fit <- eBayes(fit)
replacing the second line with
fit <- lmFit(v,design,method="robust")
is intended to robustify against observation outliers while (as Ryan
has
said) modifying the third line to
fit <- eBayes(fit,robust=TRUE)
handles dispersion outliers.
In practice, my lab uses robustified eBayes() a lot but robustified
lmFit() very little. lmFit() has included the "robust" option since
limma
was first posted to Bioconductor in 2002, but it has been found to be
seldom needed and so tends to be forgotten.
Best wishes
Gordon
> Date: Fri, 21 Mar 2014 10:27:18 -0700
> From: "Ryan C. Thompson" <rct at="" thompsonclan.org="">
> To: Ina Hoeschele <inah at="" vbi.vt.edu="">
> Cc: Bioconductor mailing list <bioconductor at="" r-project.org="">
> Subject: Re: [BioC] tagwise parameters for negative binomial
> distribution in edgeR
>
> Hi Ina,
>
> I don't think voom has any special consideration for observation
> outliers, but limma's 'eBayes' function has a 'robust' argument
which I
> believe has the same effect as the corresponding argument in edgeR's
> 'estimateDisp', i.e. dealing with outlier tags that have abnormally
> high (or low) variance.
>
> -Ryan
>
> On Fri 21 Mar 2014 08:48:23 AM PDT, Ina Hoeschele wrote:
>> Hi Mark,
>> how would the presence of observation outliers potentially
causing dispersion outliers be handled in voom?
>> Many thanks, Ina
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