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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12-05

AUTHORS

Michael I Love, Wolfgang Huber, Simon Anders

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. More... »

PAGES

550

References to SciGraph publications

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  • 2013-08-27. NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data in BMC BIOINFORMATICS
  • 2011-12-17. GC-Content Normalization for RNA-Seq Data in BMC BIOINFORMATICS
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  • Journal

    TITLE

    Genome Biology

    ISSUE

    12

    VOLUME

    15

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-014-0550-8

    DOI

    http://dx.doi.org/10.1186/s13059-014-0550-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1015222646

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/25516281


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