Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 |
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| Authors: | Michael I Love Wolfgang Huber Simon Anders |
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| Affiliation: | .Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute and Department of Biostatistics, Harvard School of Public Health, 450 Brookline Avenue, Boston, 02215 MA USA ;.Genome Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, 69117 Germany ;.Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-7314195, Berlin, Germany |
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| 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users. |
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