ldiao/Gimp: Good-Turing and empirical Bayes pseudocounts for sparse count matrices

16S rRNA sequence data is oftentimes extremely sparse, making it difficult to normalize properly for differential abundance analyses with current RNA-Seq methods, such as DESeq, edgeR, and metagenomeSeq. The count adjustment methods included here can be used to ameliorate these effects. The Good-Turing adjustment is derived from from Good-Turing frequency estimation methods. Code to generate simulated test data according to the Dirichlet-multinomial model from two different sets of parameters is also included. Further information on the methods and data can be found *IN THIS PAPER*.

Getting started

Package details

MaintainerWho to complain to <[email protected]>
LicenseGPL-3
Version0.5
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("ldiao/Gimp")
ldiao/Gimp documentation built on May 21, 2017, 10:34 a.m.