ldiao/Gimp: Good-Turing and empirical Bayes pseudocounts for sparse count matrices
Version 0.5

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.