The R package, TCC 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.
While the older version (ver. 1.1.3) of this package is currently available at the CRAN repository, we are now moving it from CRAN to Bioconductor. This webpage is temporal until the next release (perhaps, ver. 1.2.0) of TCC is available upon Bioconductor. The latest version available on this webpage is ver. 1.1.99.
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"))
User's Guide (vignette) | R script | Manual |
This package calls significant functions implemented in the other packages. This is because our normalization procedures combines normalization methods and differential expression methods established by others. For example, the TbT normalization method (Kadota et al., 2012), which is a functionality of the TCC package (Sun et al., submitted), consists of the TMM normalization method (Robinson and Oshlack, 2010) implemented in the edgeR package (Robinson et al., 2010) and an empirical Bayesian method implemented in the baySeq package (Hardcastle and Kelly, 2010). Therefore, please cite the appropriate references when you publish your results.