TCC {TCC} | R Documentation

## A package for differential expression analysis from tag count data with robust normalization strategies

### Description

This package performs differential expression analysis from transcriptome data that are produced from high-throughput sequencing (HTS) and microarray technologies. A notable feature of this package is to provide robust normalization methods whose strategy is to remove data assigned as potential differentially expressed genes (DEGs) before performing normalization for RNA-seq count data (Kadota et al., 2012; Sun et al., 2013).

### Details

TCC is a package for differential expression analysis from transcriptome data produced from RNA-seq and microarray data. This package implements some functions for calculating normalization factors, identifying DEGs, depicting so-called M-A plot, and generating simulation data.

To utilize this package, the count matrix coupled with label information should be stored to a TCC-class object using the `new` method. All functions, except for two recently added functions (i.e., `ROKU` and `WAD`) for microarray data, used in this package require this TCC-class object. Using this object, the `calcNormFactors` function calculates normalization factors and the `estimateDE` function estimates the degree of differential expression (DE) for individual genes. The estimated normalization factors obtained by using the `calcNormFactors` function are used within the statistical model for differential analysis in the `estimateDE` function. Both two functions internally call functions from other packages (edgeR, DESeq, baySeq, and EBSeq) when specified. TCC also provides some useful functions: `simulateReadCounts` for generating simulation data with various experimental designs, `plot` for depicting a M-A plot, `plotFCPseudocolor` for depicting a pseudo-color image of simulation condition that the user specified, `WAD` for identifying DEGs from two-group microarray data (single-factor design), and `ROKU` for identifying tissue-specific genes from microarray data for many tissues.

### See Also

TCC-class

### Examples

    data(hypoData)
    group <- c(1, 1, 1, 2, 2, 2)
    tcc <- new("TCC", hypoData, group)
    show(tcc)

[Package TCC version 1.22.1 Index]