How to normalize metatranscriptomic count data for differential expression analysis
نویسندگان
چکیده
منابع مشابه
How to normalize metatranscriptomic count data for differential expression analysis
BACKGROUND Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has not been clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscr...
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متن کاملTitle 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 ...
متن کاملPackage '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...
متن کاملTCC: 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...
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ژورنال
عنوان ژورنال: PeerJ
سال: 2017
ISSN: 2167-8359
DOI: 10.7717/peerj.3859