To install this package, start R and enter:

source("http://bioconductor.org/biocLite.R")
biocLite("TCC")

In most cases, you don't need to download the package archive at all.

TCC

TCC: Differential expression analysis for tag count data with robust normalization strategies

Bioconductor version: 2.13

This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in other sophisticated packages (especially edgeR, DESeq, and baySeq).

Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota

Maintainer: Jianqiang Sun <wukong at bi.a.u-tokyo.ac.jp>, Tomoaki Nishiyama <tomoakin at staff.kanazawa-u.ac.jp>

Citation (from within R, enter citation("TCC")):

Installation

To install this package, start R and enter:

source("http://bioconductor.org/biocLite.R")
biocLite("TCC")

Documentation

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

browseVignettes("TCC")

 

PDF R Script TCC
PDF   Reference Manual
Text   NEWS

Details

biocViews DifferentialExpression, HighThroughputSequencing, RNAseq, Software
Version 1.2.0
In Bioconductor since BioC 2.13 (R-3.0)
License GPL-2
Depends R (>= 2.15), methods, DESeq, edgeR, baySeq, ROC
Imports EBSeq, samr
Suggests RUnit, BiocGenerics
System Requirements
URL
Depends On Me
Imports Me
Suggests Me

Package Archives

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

Package Source TCC_1.2.0.tar.gz
Windows Binary TCC_1.2.0.zip (32- & 64-bit)
Mac OS X 10.6 (Snow Leopard) TCC_1.2.0.tgz
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