Differential expression analysis for sequence count data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-10-27

AUTHORS

Simon Anders, Wolfgang Huber

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. More... »

PAGES

r106

Journal

TITLE

Genome Biology

ISSUE

10

VOLUME

11

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/gb-2010-11-10-r106

    DOI

    http://dx.doi.org/10.1186/gb-2010-11-10-r106

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1031289083

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/20979621


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