Package: dcanr 1.28.0

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_1.28.0.tar.gz
dcanr_1.28.0.zip(r-4.7-any)dcanr_1.28.0.zip(r-4.6-any)dcanr_1.28.0.zip(r-4.5-any)
dcanr_1.28.0.tgz(r-4.6-any)dcanr_1.28.0.tgz(r-4.5-any)
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dcanr_1.28.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dcanr/json (API)

# Install 'dcanr' in R:
install.packages('dcanr', repos = c('https://bioc-release.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)

networkinferencegraphandnetworkdifferentialexpressionnetwork

7.38 score 7 stars 5 packages 38 scripts 617 downloads 3 mentions 16 exports 27 dependencies

Last updated from:3e4c32b4d8 (on RELEASE_3_23). Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksWARNING181
linux-devel-x86_64NOTE232
source / vignettesOK228
linux-release-x86_64NOTE211
macos-release-arm64NOTE115
macos-oldrel-arm64NOTE101
windows-develNOTE137
windows-releaseNOTE127
windows-oldrelNOTE105
wasm-releaseOK142

Exports:cor.pairsdcAdjustdcEvaluatedcMethodsdcNetworkdcPipelinedcScoredcTestdcZscoregetConditionNamesgetSimDatagetTrueNetworkmi.apperfMethodsperformanceMeasureplotSimNetwork

Dependencies:circlizeclicodetoolscolorspacecpp11digestdoRNGforeachGlobalOptionsglueigraphiteratorslatticelifecyclemagrittrMatrixpkgconfigplyrRColorBrewerRcppreshape2rlangrngtoolsshapestringistringrvctrs

Evaluating differential co-expression methods using dcanr
Introduction | Simulation setup used to create the data | Download the full simulated dataset | Running a pipeline on a simulation | Standard pipelines | Custom pipelines | Retrieving pre-computed results | Evaluate a pipeline | Session info
Rendered fromdcanr_evaluation_vignette.Rmdusingknitr::rmarkdown

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

Performing differential co-expression analysis using dcanr
Introduction | Installation | Available inference methods | A generic differential co-expression analysis pipeline | Load an example dataset (simulated) | Step 1: Compute scores | Step 2: Perform a statistical test | Step 3: Correcting for multiple hypothesis testing | Step 4: Generating the differential co-expression network | Session info
Rendered fromdcanr_vignette.Rmdusingknitr::rmarkdown

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