preprintbioRxiv (Cold Spring Harbor Laboratory)Jan 24, 2024GREEN OA

edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

Stem Cells Australia · The University of Melbourne

Indexed incrossref

Abstract

Abstract edgeR is an R/Bioconductor software package for differential analyses of sequencing data in the form of read counts for genes or genomic features. Over the past 15 years, edgeR has been a popular choice for statistical analysis of data from sequencing technologies such as RNA-seq or ChIP-seq. edgeR pioneered the use of the negative binomial distribution to model read count data with replicates and the use of generalized linear models to analyse complex experimental designs. edgeR implements empirical Bayes moderation methods to allow reliable inference when the number of replicates is small. This article announces edgeR version 4, which includes new developments across a range of application areas.…

Citation impact

119
total citations
FWCI
Percentile
References
75
Citations per year

Authors

5

Topics & keywords

Keywords
  • Bioconductor
  • Computer science
  • Negative binomial distribution
  • Statistical inference
  • Count data
  • Software
  • Overdispersion
  • R package
UN Sustainable Development Goals
  • Industry, innovation and infrastructure
No related works found for this paper.

Funding