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Genome Biol. 2010;11(10):R106. doi: 10.1186/gb-2010-11-10-r106. Epub 2010 Oct 27.

Differential expression analysis for sequence count data.

Author information

  • 1European Molecular Biology Laboratory, Mayerhofstraße 1, 69117 Heidelberg, Germany. sanders@fs.tum.de

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 differential 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 - indexed for MEDLINE]
PMCID:
PMC3218662
Free PMC Article
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