PoissonSeq: Significance analysis of sequencing data based on a Poisson log
linear model
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.
Version: |
1.1.2 |
Depends: |
R (≥ 2.10), combinat, splines |
Published: |
2012-10-10 |
Author: |
Jun Li |
Maintainer: |
Jun Li <jun.li at nd.edu> |
License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] (see file LICENSE) |
NeedsCompilation: |
no |
CRAN checks: |
PoissonSeq results |
Downloads: