[BioC] QuasiSeq vs DSS

[Richard Friedman]

Dear Ryan,

	Thank you for your response.
3 questions:
1. If I had just a simple pairwise comparison is it known DSS or QuasiSeq better?
2. I was unaware that an approximate implementation of QuasiSeq was available in
edgeR. If so, is it known hor  it compare to the ordinairy EdgeR on the one hand and the 
full QuasiSeq on the other.
3. And I guess that the third question is for Gordon - Is using DSS and QuasiSeq (or EdgeR) together
desireable and if so, are there plans to incorporate DSS into QuasiSeq (EdgeR).

My note was planning ahead. I will still be in the microarray world for a more few weeks
before I return to learning RNASeq. I wanted to know what the best practice is.
If you (or anybody out there) develops a script to meld the two methods, I am sure that
it would be interesting to the list.

Best wishes,
Rich





On Mar 12, 2013, at 12:59 PM, Ryan C. Thompson wrote:

> Dear Rich,
> From what I can tell, it should be possible. The development version of DESeq2 implements the DSS "squeezing" method combined with edgeR's Cox-Reid dispersion estimation. You could use DESeq2 to estimate dispersions, and then copy those dispersion values into an edgeR DGEList object. Then you can use edgeR::glmQLFTest,       which implements (approximately) the QuasiSeq method.
> I have not had time yet to investigate putting these packages together in this way, but it is something I plan to look at. I'm certain that the combination is technically possible, and I'm reasonably sure that the result would be statistically meaningful.
> -Ryan Thompson
> On Mar 12, 2013 7:06 AM, "Richard Friedman" <friedman at cancercenter.columbia.edu> wrote:
> Dear List.
> 
>         The papers on DSS (included in Bioconductor):
> 
> Wu H, Wang C, Wu Z. A new shrinkage estimator for dispersion improves
> differential expression detection in RNA-seq data. Biostatistics. 2013
> Apr;14(2):232-43.
> 
> and  QuasiSeq (included in CRAN):
> 
>  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
> 
> both give evidence of superior performance to edgeR (if I understand them correctly).
> 
> Have the two methods been compared?
> Can the 2 methods been combined (with DSS estimating the dispersion used in
> the quasi-negative bionomial disribution used in QuasiSeq)?
> 
> I would appreciate any insight with respect to what is the overall best
> method for differential expression in RNASeq available at present.
> 
> 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)/
> Columbia Initiative in Systems Biology
> Room 824
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> 1130 St. Nicholas Ave
> New York, NY 10032
> (212)851-4765 (voice)
> friedman at cancercenter.columbia.edu
> http://cancercenter.columbia.edu/~friedman/
> 
> Fritz Lang. Didn't he do "Star Trek".
> -Rose Friedman, age 16
> 
> 
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