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

[Tim Triche, Jr.]

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 at 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 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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>>
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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>
>
>


-- 
*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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