Package: dcanr 1.29.0

Dharmesh D. Bhuva

dcanr: Differential co-expression/association network analysis

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).

Authors:Dharmesh D. Bhuva [aut, cre]

dcanr_1.29.0.tar.gz
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dcanr_1.29.0.tgz(r-4.6-any)dcanr_1.29.0.tgz(r-4.5-any)
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dcanr_1.29.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
dcanr/json (API)
NEWS

# Install 'dcanr' in R:

install.packages('dcanr', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/davislaboratory/dcanr/issues

Pkgdown/docs site:https://davislaboratory.github.io

Datasets:

sim102 - Simulated expression data with knock-outs

On BioConductor:dcanr-1.29.0(bioc 3.24)dcanr-1.28.0(bioc 3.23)

networkinference graphandnetwork differentialexpression network

7.33 score 7 stars 5 packages 34 scripts 562 downloads 3 mentions 16 exports 27 dependencies

Last updated from:3f920e3adc. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	160
linux-devel-x86_64	NOTE	204
source / vignettes	OK	200
linux-release-x86_64	NOTE	215
macos-release-arm64	NOTE	118
macos-oldrel-arm64	NOTE	142
windows-devel	NOTE	124
windows-release	NOTE	125
windows-oldrel	NOTE	107
wasm-release	OK	125

Exports:cor.pairs dcAdjust dcEvaluate dcMethods dcNetwork dcPipeline dcScore dcTest dcZscore getConditionNames getSimData getTrueNetwork mi.ap perfMethods performanceMeasure plotSimNetwork

Dependencies:circlize cli codetools colorspace cpp11 digest doRNG foreach GlobalOptions glue igraph iterators lattice lifecycle magrittr Matrix pkgconfig plyr RColorBrewer Rcpp reshape2 rlang rngtools shape stringi stringr vctrs

Performing differential co-expression analysis using dcanr

Dharmesh D Bhuva

Rendered fromdcanr_vignette.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2019-04-18
Started: 2018-10-05

Evaluating differential co-expression methods using dcanr

Dharmesh D Bhuva

Rendered fromdcanr_evaluation_vignette.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2019-08-06
Started: 2018-10-05

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Fast pairwise correlation estimation	cor.pairs
Adjust for multiple testing in differential association analysis	dcAdjust
Evaluate performance of DC methods on simulations	dcEvaluate
Get names of differential co-expression methods	dcMethods
Generate a differential network from a DC analysis	dcNetwork
Run a DC pipeline on a simulation	dcPipeline
Compute scores from differential association analysis	dcScore dcScore,data.frame-method dcScore,DGEList-method dcScore,ExpressionSet-method dcScore,Matrix-method dcScore,matrix-method dcScore,SummarizedExperiment-method
Statistical test for differential association analysis	dcTest
DC analysis using the z-score method	dcZscore
Get data and conditions from a given knock-down (KD)	getConditionNames getSimData getTrueNetwork
Mutual information using adaptive partitioning	mi.ap
Get names of performance metric methods	perfMethods
Performance metrics to evaluate classification	performanceMeasure
Plot source and true differential networks from simulations	plotSimNetwork
Simulated expression data with knock-outs	sim102