Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
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This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.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
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References
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Posted ContentDOI
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Journal ArticleDOI
HTSeq—a Python framework to work with high-throughput sequencing data
TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
Journal ArticleDOI
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Bo Li,Colin N. Dewey +1 more
TL;DR: It is shown that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads, and estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired- end reads, depending on the number of possible splice forms for each gene.
Journal ArticleDOI
featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features
TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
Journal ArticleDOI
Regularization Paths for Generalized Linear Models via Coordinate Descent
TL;DR: In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.