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.
Beschreibung
This article explains the use of DESeq2, an improvement on the software DESeq. This software is used to analyze RNA-seq data through analysis of fold changes between samples. Liu et al. used DESeq to process data in both of their studies.
%0 Journal Article
%1 love2014moderated
%A Love, Michael I
%A Huber, Wolfgang
%A Anders, Simon
%D 2014
%J Genome Biology
%K 9 background methods software
%P 550
%R 10.1186/s13059-014-0550-8
%T Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
%U https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8
%V 15
%X 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.
@article{love2014moderated,
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.},
added-at = {2017-11-02T01:02:32.000+0100},
author = {Love, Michael I and Huber, Wolfgang and Anders, Simon},
biburl = {https://www.bibsonomy.org/bibtex/2e5727531b6e617fa0fc12084dc7b1e35/artheibault},
description = {This article explains the use of DESeq2, an improvement on the software DESeq. This software is used to analyze RNA-seq data through analysis of fold changes between samples. Liu et al. used DESeq to process data in both of their studies.},
doi = {10.1186/s13059-014-0550-8},
interhash = {ddf26c9817be0b82f9a256ce81415f69},
intrahash = {e5727531b6e617fa0fc12084dc7b1e35},
journal = {Genome Biology},
keywords = {9 background methods software},
month = may,
pages = 550,
timestamp = {2017-11-02T01:03:57.000+0100},
title = {Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2},
url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8},
volume = 15,
year = 2014
}