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A Powerful and Flexible Approach to the Analysis
of RNA Sequence Count Data.
Yi-Hui
Zhou
Department of Biostatistics, University of North
Carolina
Key features:
 Designed for the
differential expression analysis of the RNA-Seq
Count data.
Use Beta-Binomial model
to consider the overdispersion of the count data.
Mean-Overdispersion
modeling is used to capture the gene specific dispersion.
Supports
- Linear model.
- Covariates to account for sex, population structure,
and clinical factors.
- Outlier correction.
R implementations.
Superior performance on
tiny sample size (e.g. n1=n2=2).
Outstanding performance
on the low expressed data.
How to cite BBSeq:
If
you use BBSeq (or any dataset here) for a
publication, please use the citation
Zhou YH, Xia K, Wright FA. (2011) A
Powerful and Flexible Approach to the Analysis of RNA Sequence Count Data.
Bioinformatics. 2011 Aug 2. [Epub ahead of print]. PMID:21810900
If
you use the RNA-Seq data from the Montgomery
(2010) or Pickrell (2010) papers and obtain the
re-mapped data from
this web site,
please cite the original sources as well as the citation above.
Download
 R package
Windows version (<=R2.15); Windows version (R 3.0); Unix version.
 Reference
paper
 Manual
Vignette
Supplementary
plot
Supplementary
method
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