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

The R/Bioconductor package, TCC, provides users with a robust and accurate framework to perform differential expression (DE) analysis of tag count data. We recently developed a multi-step normalization method (TbT; Kadota et al., 2012 [3]) for two-group RNA-seq data. The strategy (called DEGES) is to remove data that are potential differentially expressed genes (DEGs) before performing the data normalization. DEGES in TCC is essential for accurate normalization of tag count data, especially when the upand down-regulated DEGs in one of the groups are extremely biased in their number. A major characteristic of TCC is to provide the DEGES-based normalization methods for several kinds of count data (two-group with or without replicates, multi-group, and so on) by virtue of the use of combinations of functions in other sophisticated packages (especially edgeR, DESeq, and baySeq). The appropriate combination provided by TCC allows a more robust and accurate estimation to be performed more easily than directly using original packages and TCC provides a simple unified interface to perform the robust normalization.