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Differential expression analysis for sequence count data.
Genome Biol. 2010 Oct 27;11(10):R106
Authors: Anders S, Huber W
ABSTRACT: High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer diferential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
PMID: 20979621 [PubMed - as supplied by publisher]
More...
Differential expression analysis for sequence count data.
Genome Biol. 2010 Oct 27;11(10):R106
Authors: Anders S, Huber W
ABSTRACT: High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer diferential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
PMID: 20979621 [PubMed - as supplied by publisher]
More...
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