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


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).


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, EBSeq, and samr) 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



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

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