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

View ORCID ProfileMichael I Love, View ORCID ProfileWolfgang Huber, View ORCID ProfileSimon Anders
doi: https://doi.org/10.1101/002832
Michael I Love
1Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
2Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute and Department of Biostatistics, Harvard School of Public Health,Boston, MA, USA
3Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
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Wolfgang Huber
1Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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Simon Anders
1Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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  • For correspondence: sanders@fs.tum.de
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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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 17, 2014.
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Michael I Love, Wolfgang Huber, Simon Anders
bioRxiv 002832; doi: https://doi.org/10.1101/002832
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Michael I Love, Wolfgang Huber, Simon Anders
bioRxiv 002832; doi: https://doi.org/10.1101/002832

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