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PMID: 25516281 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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

Genome biology ·Vol. 15 ·No. 12 ·2014-00-00 ·Pages 550

Love MI, Huber W, Anders S

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 webcite.

MeSH Terms
Algorithms Computational Biology/methods High-Throughput Nucleotide Sequencing Models, Genetic RNA/analysis Sequence Analysis, RNA Software
Chemicals
RNA
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Love Michael I
Huber Wolfgang
Anders Simon
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47 references, click to expand
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Article Info
Journal
Genome biology
Abbr.
Genome Biol
ISSN
1474-760X
DOI
10.1186/s13059-014-0550-8
Published
2014-00-00
Pages
550
Language
English
Region
England
NLM ID
100960660
PMCID
PMC4302049
Subset
IM
Grants
NCI NIH HHS · T32 CA009337 · United States
NCI NIH HHS · 5T32CA009337-33 · United States
External Links
PubMed source DOI: 10.1186/s13059-014-0550-8 PMC Full Text View journal details
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