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WGCNA (version 1.74)

Weighted Correlation Network Analysis

Description

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

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Version

Install

install.packages('WGCNA')

Monthly Downloads

17,915

Version

1.74

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

January 30th, 2026

Functions in WGCNA (1.74)

Add vertical ``guide lines'' to a dendrogram plot

Add trait information to multi-set module eigengene structure

Calculate network adjacency

Topological overlap matrix similarity and dissimilarity

Estimate the true trait underlying a list of surrogate markers.

Add grid lines to an existing plot.

TOMsimilarityFromExpr

Topological overlap matrix

Graphical representation of the Topological Overlap Matrix

accuracyMeasures

Accuracy measures for a 2x2 confusion matrix or for vectors of predicted and observed values.

Add error bars to a barplot.

Pathways with Corresponding Gene Markers - Compiled by Mike Palazzolo and Jim Wang from CHDI

Generalized Topological Overlap Measure

Align expression data with given vector

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

Biweight Midcorrelation

adjacency.polyReg

Adjacency matrix based on polynomial regression

Divide tasks among workers

Weights used in biweight midcovariance

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Calculation of biweight midcorrelations and associated p-values

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

automaticNetworkScreening

One-step automatic network gene screening

blockwiseModules

Automatic network construction and module detection

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Blue-white-red color sequence

Attempt to calculate an appropriate block size to maximize efficiency of block-wise calcualtions.

blockwiseConsensusModules

Find consensus modules across several datasets.

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

branchSplit.dissim

Branch split based on dissimilarity.

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

chooseOneHubInEachModule

Chooses a single hub gene in each module

branchSplitFromStabilityLabels

Branch split (dissimilarity) statistics derived from labels determined from a stability study

Check adjacency matrix

Clustering coefficient calculation

chooseTopHubInEachModule

Chooses the top hub gene in each module

Check structure and retrieve sizes of a group of datasets.

Fast colunm- and row-wise quantile of a matrix.

Select one representative row per group

coClustering.permutationTest

Permutation test for co-clustering

Co-clustering measure of cluster preservation between two clusterings

collapseRowsUsingKME

Selects one representative row per group based on kME

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

Iterative garbage collection.

consensusProjectiveKMeans

Consensus projective K-means (pre-)clustering of expression data

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

consensusCalculation

Calculation of a (single) consenus with optional data calibration.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

Calculate consensus kME (eigengene-based connectivities) across multiple data sets.

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

corPvalueStudent

Student asymptotic p-value for correlation

corPvalueFisher

Fisher's asymptotic p-value for correlation

correlationPreservation

Preservation of eigengene correlations

consensusRepresentatives

Consensus selection of group representatives

consensusTreeInputs

Get all elementary inputs in a consensus tree

corPredictionSuccess

Qunatification of success of gene screening

Consensus network (topological overlap).

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

Calculation of correlations and associated p-values

Fast calculations of Pearson correlation.

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

Show colors used to label modules

dynamicMergeCut

Threshold for module merging

factorizeNonNumericColumns

Turn non-numeric columns into factors

exportNetworkToVisANT

Export network data in format readable by VisANT

exportNetworkToCytoscape

Export network to Cytoscape

fixDataStructure

Put single-set data into a form useful for multiset calculations.

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

cutreeStaticColor

Constant height tree cut using color labels

Constant-height tree cut

Green-black-red color sequence

goodSamplesGenesMS

Iterative filtering of samples and genes with too many missing entries across multiple data sets

Green-white-red color sequence

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

Filter samples with too many missing entries across multiple data sets

Filter samples with too many missing entries

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Break long character strings into multiple lines

Filter genes with too many missing entries across multiple sets

Filter genes with too many missing entries

hubGeneSignificance

Hubgene significance

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

Inline display of progress

Inline display of progress

hierarchicalConsensusCalculation

Hierarchical consensus calculation

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

Impute missing data separately in each module

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Calculate individual correlation network matrices

hierarchicalConsensusKME

Calculation of measures of fuzzy module membership (KME) in hierarchical consensus modules

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

Determine whether the supplied object is a valid multiData structure

keepCommonProbes

Keep probes that are shared among given data sets

Barplot with text or color labels.

Convert numerical labels to colors.

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

Convert a list to a multiData structure and vice-versa.

Produce a labeled heatmap plot

Label scatterplot points

intramodularConnectivity

Calculation of intramodular connectivity

Relabel module labels to best match the given reference labels

matrixToNetwork

Construct a network from a matrix

moduleEigengenes

Calculate module eigengenes.

Fast joint calculation of row- or column-wise minima and indices of minimum elements

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

mergeCloseModules

Merge close modules in gene expression data

lowerTri2matrix

Reconstruct a symmetric matrix from a distance (lower-triangular) representation

modifiedBisquareWeights

Modified Bisquare Weights

Meta-analysis Z statistic

Meta-analysis of binary and continuous variables

Turn a multiData structure into a single matrix or data frame.

moduleMergeUsingKME

Merge modules and reassign genes using kME.

Subset rows and columns in a multiData structure

Apply a function to each set in a multiData structure.

Fixed-height cut of a dendrogram.

Apply a function to elements of given multiData structures.

If possible, simplify a multiData structure to a 3-dimensional array.

mtd.setColnames

Get and set column names in a multiData structure.

Set attributes on each component of a multiData structure

modulePreservation

Calculation of module preservation statistics

nearestCentroidPredictor

Nearest centroid predictor

Number of sets in a multi-set variable

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

Calculate module eigengenes.

Union and intersection of multiple sets

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

Analogs of grep(l) and (g)sub for multiple patterns and relacements

Create a multiData structure.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

Number of present data entries.

newConsensusOptions

Create a list holding consensus calculation options.

newConsensusTree

Create a new consensus tree

nearestNeighborConnectivityMS

Connectivity to a constant number of nearest neighbors across multiple data sets

networkScreening

Identification of genes related to a trait

newCorrelationOptions

Creates a list of correlation options.

newNetworkOptions

Create a list of network construction arguments (options).

newBlockwiseData

Create, merge and expand BlockwiseData objects

networkScreeningGS

Network gene screening with an external gene significance measure

newBlockInformation

Create a list holding information about dividing data into blocks

networkConcepts

Calculations of network concepts

normalizeLabels

Transform numerical labels into normal order.

Color representation for a numeric variable

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

overlapTableUsingKME

Determines significant overlap between modules in two networks based on kME tables.

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

Calculate overlap of modules

Put close eigenvectors next to each other

plotModuleSignificance

Barplot of module significance

plotColorUnderTree

Plot color rows in a given order, for example under a dendrogram

Red and Green Color Image of Correlation Matrix

plotNetworkHeatmap

Network heatmap plot

Plot multiple histograms in a single plot

Red and Green Color Image of Data Matrix

plotDendroAndColors

Dendrogram plot with color annotation of objects

plotEigengeneNetworks

Eigengene network plot

Pairwise scatterplots of eigengenes

populationMeansInAdmixture

Estimate the population-specific mean values in an admixed population.

projectiveKMeans

Projective K-means (pre-)clustering of expression data

propVarExplained

Proportion of variance explained by eigengenes.

pruneConsensusModules

Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity

Prepend a comma to a non-empty string

Parallel quantile, median, mean

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

Estimate the q-values for a given set of p-values

proportionsInAdmixture

Estimate the proportion of pure populations in an admixed population based on marker expression values.

preservationNetworkConnectivity

Network preservation calculations

Pad numbers with leading zeros to specified total width

qvalue.restricted

qvalue convenience wrapper

Red-white-green color sequence

removePrincipalComponents

Remove leading principal components from data

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Estimate the p-value for ranking consistently high (or low) on multiple lists

Replace missing values with a constant.

relativeCorPredictionSuccess

Compare prediction success

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

Rand index of two partitions

Removes the grey eigengene from a given collection of eigengenes.

sampledBlockwiseModules

Blockwise module identification in sampled data

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

Shorten given character strings by truncating at a suitable separator.

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Red and Green Color Specification

Visual check of scale-free topology

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

setCorrelationPreservation

Summary correlation preservation measure

simpleConsensusCalculation

Simple calculation of a single consenus

simulateDatExpr5Modules

Simplified simulation of expression data

Simulate a gene co-expression module

Signed eigengene-based connectivity

simulateMultiExpr

Simulate multi-set expression data

simulateEigengeneNetwork

Simulate eigengene network from a causal model

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

simulateDatExpr

Simulation of expression data

signumAdjacencyFunction

Hard-thresholding adjacency function

Round numeric columns to given significant digits.

Space-less paste

standardScreeningBinaryTrait

Standard screening for binatry traits

simulateSmallLayer

Simulate small modules

standardScreeningNumericTrait

Standard screening for numeric traits

Opens a graphics window with specified dimensions

Standard error of the mean of a given vector.

softConnectivity

Calculates connectivity of a weighted network.

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

sizeRestrictedClusterMerge

Cluter merging with size restrictions

Colors this library uses for labeling modules.

Topological overlap for a subset of a whole set of genes

unsignedAdjacency

Calculation of unsigned adjacency

stratifiedBarplot

Bar plots of data across two splitting parameters

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

Boxplot annotated by a Kruskal-Wallis p-value

Barplot with error bars, annotated by Kruskal-Wallis or ANOVA p-value

userListEnrichment

Measure enrichment between inputted and user-defined lists

transposeBigData

Transpose a big matrix or data frame

Topological overlap for a subset of the whole set of genes

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

Scatterplot with density

votingLinearPredictor

Voting linear predictor

verboseScatterplot

Scatterplot annotated by regression line and p-value

GOenrichmentAnalysis

Calculation of GO enrichment

Stem Cell-Related Genes with Corresponding Gene Markers

Brain-Related Categories with Corresponding Gene Markers

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

Various basic operations on BlockwiseData objects.

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

Blood Cell Types with Corresponding Gene Markers