A normalization strategy for comparing tag count data

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...

Title Tcc: Differential Expression Analysis for Tag Count Data with Robust Normalization Strategies

December 22, 2016 Type Package Title TCC: Differential expression analysis for tag count data with robust normalization strategies Version 1.14.0 Author Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer Jianqiang Sun , Tomoaki Nishiyama Description This package provides a series of functions for performing ...

Package 'tcc' Title Tcc: Differential Expression Analysis for Tag Count Data with Robust Normalization Strategies

April 26, 2017 Type Package Title TCC: Differential expression analysis for tag count data with robust normalization strategies Version 1.16.0 Author Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer Jianqiang Sun , Tomoaki Nishiyama Description This package provides a series of functions for performing dif...

cDNA Microarray Data Normalization

CrossNorm: a novel normalization strategy for microarray data in cancers.

Normalization is essential to get rid of biases in microarray data for their accurate analysis. Existing normalization methods for microarray gene expression data commonly assume a similar global expression pattern among samples being studied. However, scenarios of global shifts in gene expressions are dominant in cancers, making the assumption invalid. To alleviate the problem, here we propose...