Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Love, Michael I.; Huber, Wolfgang; Anders, Simon

Affiliations

¹Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute and Department of Biostatistics, Harvard School of Public Health, 450 Brookline Avenue, Boston, MA 02215, USA

²Genome Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany

³Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.

Abstract

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.

Links

DOI: 10.1186/s13059-014-0550-8

PubMed: 25516281

PubMedCentral: PMC4302049

WoS: 000346609500022

History

Received: 2014-05-27

Accepted: 2014-11-9

Online: 2014-12-05

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