baySeq

DOI: 10.18129/B9.bioc.baySeq    

Empirical Bayesian analysis of patterns of differential expression in count data

Bioconductor version: Release (3.13)

This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

Author: Thomas J. Hardcastle

Maintainer: Thomas J. Hardcastle <tjh48 at cam.ac.uk>

Citation (from within R, enter citation("baySeq")):

Installation

To install this package, start R (version "4.1") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("baySeq")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("baySeq")

 

PDF R Script Advanced baySeq analyses
PDF R Script baySeq
PDF   Reference Manual

Details

biocViews DifferentialExpression, MultipleComparison, SAGE, Sequencing, Software
Version 2.26.0
In Bioconductor since BioC 2.5 (R-2.10) (12 years)
License GPL-3
Depends R (>= 2.3.0), methods, GenomicRanges, abind, parallel
Imports edgeR
LinkingTo
Suggests BiocStyle, BiocGenerics
SystemRequirements
Enhances
URL
Depends On Me clusterSeq, EDDA, Rcade, segmentSeq, TCC
Imports Me EDDA, metaseqR2, riboSeqR, srnadiff
Suggests Me compcodeR
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package baySeq_2.26.0.tar.gz
Windows Binary baySeq_2.26.0.zip
macOS 10.13 (High Sierra) baySeq_2.26.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/baySeq
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/baySeq
Package Short Url https://bioconductor.org/packages/baySeq/
Package Downloads Report Download Stats

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