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.

Getting started

Package details

AuthorJun Li
MaintainerJun Li <jun.li@nd.edu>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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PoissonSeq documentation built on May 1, 2019, 7:33 p.m.