Tim,

	Thanks. 

Jean,

	Sorry.

I thought aec might have meant Gordon's paper on QuasiSeq and there is
was a paper by Wu and Smyth write after the QuasiSeq on Pubmed. 
I looked for a Wu Z paper but there were so many,

Anyway, any thoughts about how the method cited compares with  QuasiSeq
would be appreciated.

Best wishes,
Rich

On Nov 26, 2012, at 1:24 PM, Tim Triche, Jr. wrote:

> The supplement mentioned by the authors in their final paragraph is also attached (here).  It is worth at least a glance.  I wonder whether the notoriously anticonservative Wald test is responsible for recovering information lost to over-shrinkage.  
> 
> 
> 
> On Mon, Nov 26, 2012 at 10:20 AM, Tim Triche, Jr. <tim.triche@gmail.com> wrote:
> 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@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@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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> 
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> 
> 
> 
> -- 
> A model is a lie that helps you see the truth.
> 
> Howard Skipper
> 
> 
> 
> 
> -- 
> A model is a lie that helps you see the truth.
> 
> Howard Skipper
> 
> <kxs033supp.pdf>


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