[BioC] QuasiSeq vs DSS

[Richard Friedman]

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
Irving Cancer Research Center
Columbia University
1130 St. Nicholas Ave
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(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