OliverVoogd/edgeR: Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE.

Getting started

Package details

AuthorYunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
Bioconductor views AlternativeSplicing BatchEffect Bayesian BiomedicalInformatics CellBiology ChIPSeq Clustering Coverage DNAMethylation DifferentialExpression DifferentialMethylation DifferentialSplicing Epigenetics FunctionalGenomics GeneExpression GeneSetEnrichment Genetics ImmunoOncology MultipleComparison Normalization Pathways QualityControl RNASeq Regression SAGE Sequencing SystemsBiology TimeCourse Transcription Transcriptomics
MaintainerYunshun Chen <yuchen@wehi.edu.au>, Gordon Smyth <smyth@wehi.edu.au>, Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>, Mark Robinson <mark.robinson@imls.uzh.ch>
LicenseGPL (>=2)
URL http://bioinf.wehi.edu.au/edgeR https://bioconductor.org/packages/edgeR
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
OliverVoogd/edgeR documentation built on July 28, 2022, 10:13 p.m.