Moderated Estimation of Fold Change and Dispersion for RNA-seq Data With DESeq2

Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • High-Throughput Nucleotide Sequencing
  • Models, Genetic
  • RNA / analysis*
  • Sequence Analysis, RNA
  • Software*

Substances

  • RNA