Significance analysis of sequencing data based on a Poisson log
linear model
R package PoissonSeq version 1.1.2
This package implements a method for normalization,
testing, and false discovery rate estimation for RNA-sequencing
data. The description of the method is in Li J, Witten DM,
Johnstone I, Tibshirani R (2012). Normalization, testing, and
false discovery rate estimation for RNA-sequencing data.
Biostatistics 13(3): 523-38. We estimate the sequencing depths
of experiments using a new method based on Poisson
goodness-of-fit statistic, calculate a score statistic on the
basis of a Poisson log-linear model, and then estimate the
false discovery rate using a modified version of permutation
plug-in method. A more detailed instruction as well as sample
data is available at
http://www.stanford.edu/~junli07/research.html. In this
version, we changed the way of calculating log foldchange for
two-class data. The FDR estimation part remains unchanged.
Software
Depends: R(>= 2.10),combinat,splines
Jun Li <jun.li@nd.edu>
Comprehensive R Archive Network (CRAN)
Jun Li
GPL (>= 2)
2012-10-10
application/tgz
https://CRAN.R-project.org/package=PoissonSeq