TCC: an R package for comparing tag count data with robust normalization strategies

The R package, TCC (Sun et al., 2013) provides users with a robust and accurate framework to perform differential expression analysis of tag count data. Differential expression analysis of tag count data (such as RNA-seq) from high-throughput sequencing technologies is a fundamental means of studying gene expression. We recently developed a multi-step normalization method (TbT; Kadota et al., 2012) for two-group RNA-seq data with replicates. The strategy is to remove data that are potential differentially expressed genes (DEGs) before performing the data normalization. We demonstrated that the DEG elimination strategy (called DEGES) for data normalization is essential for obtaining a well-ranked gene list in which true DEGs are top-ranked and non-DEGs are bottom ranked. TCC provides integrated analysis pipelines with improved data normalization steps, compared with other packages such as edgeR, DESeq, and baySeq, by appropriately combining their functionalities.

Important note! (last modified: Jul 7, 2013)

While the older version (ver. 1.1.3) of this package is currently available at the CRAN repository, the next version (TCC ver. 1.2.0) will be available from Bioconductor. This webpage is temporal until the next release (perhaps, ver. 1.2.0) of TCC is available upon Bioconductor (ver. 2.13; perhaps, Oct 2013). The latest version available on this webpage is ver. 1.1.99.

Installation

To install the latest version (ver. 1.1.99) of this package, download the source file and enter the following command after starting R:

install.packages("TCC_1.1.99.tar.gz", repos = NULL, type = "source")

Note that you need to enter the following commands if those packages have not been installed in your R environment:

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

biocLite(c("edgeR", "baySeq", "DESeq", "ROC"))




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