[BioC] Fwd: Conservative results using DEXSeq

[Levi Waldron]

I have noticed the kind of p-value histograms that Gu describes in other
situations also, even using the same technologies and bioinformatic methods
as other situations where it doesn't occur.  I am not sure why it happened,
but it could have to do with a batch effect that is *not* confounded with
the outcome variable?

As an example I'm attaching raw p-value histograms of Cox regressions for
each of 14 ovarian cancer datasets, code below.  At least one of these has
the monotonic increase described.  This experiment used the same microarray
platform as many of the other datasets (Affy hgu133plus2), but is the only
experiment using microdissected tissues.  Point is just that the effect
could be magnified some reason relating to the experiment.

library(survival)
library(affy)
library(curatedOvarianData)
if( !require("survHD") || packageVersion("survHD") != "0.99.1" ){
    library(devtools)
    install_url("
https://bitbucket.org/lwaldron/survhd/downloads/survHD_0.99.1.tar.gz")
}


source(system.file("extdata",
"patientselection.config",package="curatedOvarianData"))
source(system.file("extdata", "createEsetList.R", package =
"curatedOvarianData"))

pvals <- lapply(esets, function(eset) rowCoxTests(exprs(eset), eset$y)[, 3])

png("Cox_p-values.png")
par(mfrow=c(4, 4))
for (i in 1:length(pvals))
    hist(pvals[[i]], main=names(pvals)[i], xlab="raw p-value")
dev.off()



On Wed, Jul 24, 2013 at 3:55 AM, Simon Anders <anders at embl.de> wrote:

> Hi
>
>
> On 23/07/13 14:47, Gu [guest] wrote:
>
>> By checking the histogram of raw p-values of exons (NOT genes), I
>> find that it is monotonically increasing from 0 to 1, with relatively
>> few counting bins falling into the bins from 0 to 0.2.
>>
>
> You are right, DEXSeq sometimes tends to be overly conservative, which
> then results in a skewed p value histogram as you describe it. Usually, it
> is, however, only a rather slight skew, and it seems that the performance
> is unusually bad for your specific dataset.
>
> The main reason for the conservative results is the way we estimate
> dispersion. Since the release of DEXSeq, we have made quite some progress
> in improving the dispersion estimation by now using an empirical-Bayes
> shrinkage estimator, and DESeq2 now offers a much better solution, at least
> for gene-level tests. We are working on applying the same changes to
> DEXSeq, and this should solve your issue. I'm afraid, however, that I have
> to ask you for some patience until we are finished with these changes.
>
>   Simon
>
>
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