Package: multiWGCNA 1.11.0

Dario Tommasini

multiWGCNA: multiWGCNA

An R package for deeping mining gene co-expression networks in multi-trait expression data. Provides functions for analyzing, comparing, and visualizing WGCNA networks across conditions. multiWGCNA was designed to handle the common case where there are multiple biologically meaningful sample traits, such as disease vs wildtype across development or anatomical region.

Authors:Dario Tommasini [aut, cre], Brent Fogel [aut, ctb]

multiWGCNA_1.11.0.tar.gz
multiWGCNA_1.11.0.zip(r-4.7)multiWGCNA_1.11.0.zip(r-4.6)multiWGCNA_1.11.0.zip(r-4.5)
multiWGCNA_1.11.0.tgz(r-4.6-any)multiWGCNA_1.11.0.tgz(r-4.5-any)
multiWGCNA_1.11.0.tar.gz(r-4.7-any)multiWGCNA_1.11.0.tar.gz(r-4.6-any)
multiWGCNA_1.11.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
multiWGCNA/json (API)

# Install 'multiWGCNA' in R:

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

Datasets:

permutationTestResults - Permutation test results

On BioConductor:multiWGCNA-1.11.0(bioc 3.24)multiWGCNA-1.10.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

sequencing rnaseq geneexpression differentialexpression regression clustering

4.90 score 9 scripts 346 downloads 33 exports 126 dependencies

Last updated from:928cc00af6. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	231
linux-devel-x86_64	NOTE	627
source / vignettes	OK	510
linux-release-x86_64	NOTE	710
macos-release-arm64	NOTE	546
macos-oldrel-arm64	NOTE	354
windows-devel	NOTE	834
windows-release	NOTE	535
windows-oldrel	NOTE	1038
wasm-release	OK	181

Exports:bidirectionalBestMatches BuildTOMFlowDF cleanDatExpr coexpressionLineGraph computeOverlapsFromWGCNA constructNetworks diffCoexpression diffModuleExpression drawMultiWGCNAnetwork GetDatExpr getModule getPreservation GetSignificantOverlap iterate makeTraitTable makeTraitTable2 moduleComparisonPlot moduleExpressionPlot ModuleFlowPlot ModuleFlowPlot2Way moduleToModuleHeatmap name overlapComparisons performANOVA PlotMultiNodesTOMflow preservationComparisonPlot preservationComparisons PreservationPermutationTest PreservationScoreDistribution runDME summarizeResults TOMFlowPlot topNGenes

Dependencies:abind backports base64enc Biobase BiocGenerics bit bit64 bslib cachem checkmate circlize cli clipr cluster codetools colorspace cowplot cpp11 crayon data.table dcanr DelayedArray digest doParallel doRNG dplyr dynamicTreeCut evaluate farver fastcluster fastmap flashClust fontawesome foreach foreign Formula fs generics GenomicRanges ggalluvial ggforce ggplot2 ggraph ggrepel GlobalOptions glue graphlayouts gridExtra gtable highr Hmisc hms htmlTable htmltools htmlwidgets igraph impute IRanges isoband iterators jquerylib jsonlite knitr labeling lattice lazyeval lifecycle magrittr MASS Matrix MatrixGenerics matrixStats memoise mime nnet patchwork pillar pkgconfig plyr polyclip preprocessCore prettyunits progress purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo readr reshape2 rlang rmarkdown rngtools rpart rstudioapi S4Arrays S4Vectors S7 sass scales Seqinfo shape SparseArray stringi stringr SummarizedExperiment survival systemfonts tibble tidygraph tidyr tidyselect tinytex tweenr tzdb utf8 vctrs viridis viridisLite vroom WGCNA withr xfun XVector yaml

multiWGCNA: visualizing condition-specific networks

Rendered fromastrocyte_map_v2.Rmdusingknitr::rmarkdown

Last update: 2025-11-26
Started: 2023-06-06

multiWGCNA: the full workflow

Introduction | Install the multiWGCNA R package | Load microarray data from human post-mortem brains | Perform network construction, module eigengene calculation, module-trait correlation | Compare modules by overlap across conditions | Perform differential module expression analysis | Perform the module preservation analysis | Summarize interesting results from the analyses | Print the session info

Rendered fromautism_full_workflow.Rmdusingknitr::rmarkdown

Last update: 2025-11-26
Started: 2023-06-30

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Best matching modules	bidirectionalBestMatches
BuildTOMFlowDF	BuildTOMFlowDF
cleanDatExpr	cleanDatExpr
Coexpression Line Graph	coexpressionLineGraph
computeOverlapsFromWGCNA	computeOverlapsFromWGCNA
constructNetworks: Construct all the weighted gene correlation networks	constructNetworks
Differential co-expresison analysis	diffCoexpression
Differential module expression	diffModuleExpression
Draw multiWGCNA network	drawMultiWGCNAnetwork
Get expression data	GetDatExpr
name: Name of WGCNAobject	getModule
getPreservation	getPreservation
Get significant overlap	GetSignificantOverlap
iterate: Iterate function across networks	iterate
Generate a trait table from a sample table	makeTraitTable
Generate a trait table from a sample table (version 2)	makeTraitTable2
Module comparison plot	moduleComparisonPlot
Plots an expression profile for a module	moduleExpressionPlot
Module sankey diagram	ModuleFlowPlot
Module sankey diagram	ModuleFlowPlot2Way
Module to module heatmap	moduleToModuleHeatmap
name: Name of WGCNAobject	name
Overlap comparisons	overlapComparisons
Perform ANOVA	performANOVA
Permutation test results	permutationTestResults
PlotMultiNodesTOMflow	PlotMultiNodesTOMflow
Preservation Comparison Scatterplot	preservationComparisonPlot
Preservation comparisons	preservationComparisons
PreservationPermutationTest	PreservationPermutationTest
PreservationScoreDistribution	PreservationScoreDistribution
Run differential module expression	runDME
summarizeResults: Summarize results from a results list object	summarizeResults
TOMFlowPlot	TOMFlowPlot
topNGenes: Top N genes of a module	topNGenes
The WGCNA Class	WGCNA-class