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Differential co-expression/association network analysis

Bioconductor version: Release (3.19)

This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease).

Author: Dharmesh D. Bhuva [aut, cre]

Maintainer: Dharmesh D. Bhuva <bhuva.d at>

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


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


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


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

1. Differential co-expression analysis HTML R Script
2. DC method evaluation HTML R Script
Reference Manual PDF


biocViews DifferentialExpression, GraphAndNetwork, Network, NetworkInference, Software
Version 1.20.0
In Bioconductor since BioC 3.9 (R-3.6) (5 years)
License GPL-3
Depends R (>= 3.6.0)
Imports igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG
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Suggests EBcoexpress, testthat, EBarrays, GeneNet, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR
Linking To
Enhances parallel, doSNOW, doParallel
Depends On Me
Imports Me multiWGCNA, SingscoreAMLMutations
Suggests Me
Links To Me
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Follow Installation instructions to use this package in your R session.

Source Package dcanr_1.20.0.tar.gz
Windows Binary
macOS Binary (x86_64) dcanr_1.20.0.tgz
macOS Binary (arm64) dcanr_1.20.0.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
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