Linnorm

This package is for version 3.3 of Bioconductor; for the stable, up-to-date release version, see Linnorm.

Linear model and normality based transformation method (Linnorm)

Bioconductor version: 3.3

Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. Its main function is to normalize and transform these datasets for parametric tests. Examples of parametric tests include using limma for differential expression analysis or differential peak detection, or calculating Pearson correlation coefficient for gene correlation study. Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, Linnorm provides the RnaXSim function for the simulation of RNA-seq raw counts for the evaluation of differential expression analysis methods. RnaXSim can simulate RNA-seq dataset in Gamma, Log Normal, Negative Binomial or Poisson distributions.

Author: Shun Hang Yip <shunyip at bu.edu>, Panwen Wang <pwwang at pwwang.com>, Jean-Pierre Kocher <Kocher.JeanPierre at mayo.edu>, Pak Chung Sham <pcsham at hku.hk>, Junwen Wang <junwen at uw.edu>

Maintainer: Ken Shun Hang Yip <shunyip at bu.edu>

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("Linnorm")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("Linnorm")

Details

Package Archives

Follow Installation instructions to use this package in your R session.