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.
Description
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. - PubMed - NCBI
%0 Journal Article
%1 Love:2014:Genome-Biol:25516281
%A Love, M I
%A Huber, W
%A Anders, S
%D 2014
%J Genome Biol
%K MUSTREAD bioconductor bioinformatics deseq differential-expression fpkm normalization rna-seq rpkm software
%N 12
%P 550-550
%R 10.1186/s13059-014-0550-8
%T Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
%U https://www.ncbi.nlm.nih.gov/pubmed/25516281
%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 webcite.
@article{Love:2014:Genome-Biol:25516281,
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.},
added-at = {2018-10-06T22:44:53.000+0200},
author = {Love, M I and Huber, W and Anders, S},
biburl = {https://www.bibsonomy.org/bibtex/20499d28f9c10ed63559a923531f4b1bb/marcsaric},
description = {Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. - PubMed - NCBI},
doi = {10.1186/s13059-014-0550-8},
interhash = {ddf26c9817be0b82f9a256ce81415f69},
intrahash = {0499d28f9c10ed63559a923531f4b1bb},
journal = {Genome Biol},
keywords = {MUSTREAD bioconductor bioinformatics deseq differential-expression fpkm normalization rna-seq rpkm software},
number = 12,
pages = {550-550},
pmid = {25516281},
timestamp = {2018-10-17T21:55:25.000+0200},
title = {Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2},
url = {https://www.ncbi.nlm.nih.gov/pubmed/25516281},
volume = 15,
year = 2014
}