[BioC] normalization method & dispersion estimation RNA-seq data

Tim Triche, Jr. tim.triche at gmail.com
Mon Nov 26 19:20:28 CET 2012


I think he means

A new shrinkage estimator for dispersion improves differential expression
detection in RNA-seq data.
http://www.ncbi.nlm.nih.gov/pubmed/23001152

which outlines an empirical Bayes method to improve estimation of the Gamma
parameters in the Gamma-Poisson (i.e. negative binomial) formulation of the
count model.  Other authors have proposed generalized Poisson,
beta-binomial, Laplace mixture models, etc. for similar purposes, and Dr.
Smyth has presented extensive empirical results for the existing edgeR
formulations via Cox-Reid estimation along a sliding scale from
"individual" to "completely shared" variance (Gamma).

On the other hand, the authors (Hao Wu and Jean Wu, at least) are Hopkins
alumni, if I'm not mistaken, and the corresponding author wrote the SQN
package, so I can't imagine an implementation of DSS for use in edgeR is
too terribly far off.  However:

We find that most of the improvement obtained in DSS is due to the
different dispersion estimate, as
passing the DSS estimates to edgeR/DESeq yields very similar results as in
DSS (supplementary material
available at Biostatistics online, Figure S9). Both the edgeR and the DESeq
methods have been expanded
to now accommodate multiclass comparisons. Our test is currently limited to
two-class comparison and it
is our immediate plan to extend the dispersion estimators to multifactor
designs. With an estimate of the
dispersion, one can use generalized linear models as done in McCarthy and
others (2012).

The paper is open-access and thus attached.  Perhaps the authors (of any of
the above works) can comment.


On Mon, Nov 26, 2012 at 9:38 AM, Richard Friedman <
friedman at cancercenter.columbia.edu> wrote:

>
> On Nov 26, 2012, at 12:30 PM, aec [guest] wrote:
>
>
>
> >
> > Is it possible to adopt the CQN normalization method (Hansen, 2012) as
> an option of the edgeR function 'calcNormFactors' ? And the new shrinkage
> estimator for dispersion (Wu, 2012) that seems to be better than the
> currently used by edgeR ?
> >
> > thanks,
> >
> >
>
> Dear Aec,
>
>         Is it possible you mean this paper:
>
> Lund SP, Nettleton D, McCarthy DJ, Smyth GK. Detecting Differential
> Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken
> Dispersion Estimates.
> Stat Appl Genet Mol Biol. 2012 Oct 22;11(5). doi:pii:
> /j/sagmb.2012.11.issue-5/1544-6115.1826/1544-6115.1826.xml.
> 10.1515/1544-6115.1826. PubMed PMID: 23104842.
> If not, please give the complete reference to the Wu paper.
> Thanks and best wishes,
> Rich
> Richard A. Friedman, PhD
> Associate Research Scientist,
> Biomedical Informatics Shared Resource
> Herbert Irving Comprehensive Cancer Center (HICCC)
> Lecturer,
> Department of Biomedical Informatics (DBMI)
> Educational Coordinator,
> Center for Computational Biology and Bioinformatics (C2B2)/
> National Center for Multiscale Analysis of Genomic Networks (MAGNet)
> Room 824
> Irving Cancer Research Center
> Columbia University
> 1130 St. Nicholas Ave
> New York, NY 10032
> (212)851-4765 (voice)
> friedman at cancercenter.columbia.edu
> http://cancercenter.columbia.edu/~friedman/
>
> In memoriam, Ray Bradbury
>
>
>
>
>
>
> > -- output of sessionInfo():
> >
> > any
> >
> > --
> > Sent via the guest posting facility at bioconductor.org.
> >
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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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