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WGCNA (version 1.73)

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

16,961

Version

1.73

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

September 18th, 2024

Functions in WGCNA (1.73)

TOMsimilarityFromExpr

Topological overlap matrix

Add trait information to multi-set module eigengene structure

accuracyMeasures

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

Graphical representation of the Topological Overlap Matrix

Add error bars to a barplot.

Estimate the true trait underlying a list of surrogate markers.

Calculate network adjacency

Topological overlap matrix similarity and dissimilarity

Divide tasks among workers

Align expression data with given vector

Weights used in biweight midcovariance

Calculation of biweight midcorrelations and associated p-values

Biweight Midcorrelation

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

adjacency.polyReg

Adjacency matrix based on polynomial regression

Add grid lines to an existing plot.

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

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

automaticNetworkScreening

One-step automatic network gene screening

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

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

blockwiseModules

Automatic network construction and module detection

Blue-white-red color sequence

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

blockwiseConsensusModules

Find consensus modules across several datasets.

branchSplit.dissim

Branch split based on dissimilarity.

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

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

chooseTopHubInEachModule

Chooses the top hub gene in each module

branchSplitFromStabilityLabels

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

Check structure and retrieve sizes of a group of datasets.

Check adjacency matrix

Select one representative row per group

chooseOneHubInEachModule

Chooses a single hub gene in each module

Co-clustering measure of cluster preservation between two clusterings

coClustering.permutationTest

Permutation test for co-clustering

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

collapseRowsUsingKME

Selects one representative row per group based on kME

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

Clustering coefficient calculation

consensusCalculation

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

Iterative garbage collection.

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

consensusProjectiveKMeans

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

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

Calculation of correlations and associated p-values

Fast calculations of Pearson correlation.

consensusTreeInputs

Get all elementary inputs in a consensus tree

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

corPredictionSuccess

Qunatification of success of gene screening

corPvalueFisher

Fisher's asymptotic p-value for correlation

factorizeNonNumericColumns

Turn non-numeric columns into factors

corPvalueStudent

Student asymptotic p-value for correlation

consensusRepresentatives

Consensus selection of group representatives

Consensus network (topological overlap).

fixDataStructure

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

cutreeStaticColor

Constant height tree cut using color labels

correlationPreservation

Preservation of eigengene correlations

Show colors used to label modules

exportNetworkToCytoscape

Export network to Cytoscape

exportNetworkToVisANT

Export network data in format readable by VisANT

Filter genes with too many missing entries

Filter samples with too many missing entries across multiple data sets

goodSamplesGenesMS

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

Filter genes with too many missing entries across multiple sets

dynamicMergeCut

Threshold for module merging

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Filter samples with too many missing entries

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

Green-black-red color sequence

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

Green-white-red color sequence

Constant-height tree cut

Break long character strings into multiple lines

Calculate individual correlation network matrices

Impute missing data separately in each module

hubGeneSignificance

Hubgene significance

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

hierarchicalConsensusCalculation

Hierarchical consensus calculation

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

hierarchicalConsensusKME

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

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Inline display of progress

Inline display of progress

Barplot with text or color labels.

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

intramodularConnectivity

Calculation of intramodular connectivity

Label scatterplot points

Produce a labeled heatmap plot

Convert numerical labels to colors.

keepCommonProbes

Keep probes that are shared among given data sets

Determine whether the supplied object is a valid multiData structure

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

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

matrixToNetwork

Construct a network from a matrix

mergeCloseModules

Merge close modules in gene expression data

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

modifiedBisquareWeights

Modified Bisquare Weights

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

moduleEigengenes

Calculate module eigengenes.

lowerTri2matrix

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

Relabel module labels to best match the given reference labels

Meta-analysis Z statistic

mtd.setColnames

Get and set column names in a multiData structure.

Set attributes on each component of a multiData structure

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

Meta-analysis of binary and continuous variables

Apply a function to each set in a multiData structure.

Apply a function to elements of given multiData structures.

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

modulePreservation

Calculation of module preservation statistics

moduleMergeUsingKME

Merge modules and reassign genes using kME.

Subset rows and columns in a multiData structure

Fixed-height cut of a dendrogram.

Number of present data entries.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

nearestCentroidPredictor

Nearest centroid predictor

Create a multiData structure.

Union and intersection of multiple sets

networkScreening

Identification of genes related to a trait

Number of sets in a multi-set variable

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

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

Calculate module eigengenes.

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

networkConcepts

Calculations of network concepts

newConsensusTree

Create a new consensus tree

nearestNeighborConnectivityMS

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

newConsensusOptions

Create a list holding consensus calculation options.

newNetworkOptions

Create a list of network construction arguments (options).

newCorrelationOptions

Creates a list of correlation options.

networkScreeningGS

Network gene screening with an external gene significance measure

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

newBlockInformation

Create a list holding information about dividing data into blocks

normalizeLabels

Transform numerical labels into normal order.

overlapTableUsingKME

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

newBlockwiseData

Create, merge and expand BlockwiseData objects

Color representation for a numeric variable

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Put close eigenvectors next to each other

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

plotDendroAndColors

Dendrogram plot with color annotation of objects

Calculate overlap of modules

plotEigengeneNetworks

Eigengene network plot

populationMeansInAdmixture

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

plotNetworkHeatmap

Network heatmap plot

Plot multiple histograms in a single plot

plotModuleSignificance

Barplot of module significance

Red and Green Color Image of Correlation Matrix

pruneConsensusModules

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

plotColorUnderTree

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

Pairwise scatterplots of eigengenes

Parallel quantile, median, mean

propVarExplained

Proportion of variance explained by eigengenes.

Pad numbers with leading zeros to specified total width

Red and Green Color Image of Data Matrix

projectiveKMeans

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

Prepend a comma to a non-empty string

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

proportionsInAdmixture

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

preservationNetworkConnectivity

Network preservation calculations

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

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

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

Shorten given character strings by truncating at a suitable separator.

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

sampledBlockwiseModules

Blockwise module identification in sampled data

Replace missing values with a constant.

removePrincipalComponents

Remove leading principal components from data

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

relativeCorPredictionSuccess

Compare prediction success

Visual check of scale-free topology

Removes the grey eigengene from a given collection of eigengenes.

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

qvalue.restricted

qvalue convenience wrapper

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

simulateDatExpr

Simulation of expression data

signumAdjacencyFunction

Hard-thresholding adjacency function

Signed eigengene-based connectivity

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

Rand index of two partitions

Red and Green Color Specification

simpleConsensusCalculation

Simple calculation of a single consenus

Simulate a gene co-expression module

softConnectivity

Calculates connectivity of a weighted network.

Red-white-green color sequence

Round numeric columns to given significant digits.

stratifiedBarplot

Bar plots of data across two splitting parameters

sizeRestrictedClusterMerge

Cluter merging with size restrictions

userListEnrichment

Measure enrichment between inputted and user-defined lists

simulateMultiExpr

Simulate multi-set expression data

Standard error of the mean of a given vector.

setCorrelationPreservation

Summary correlation preservation measure

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

simulateDatExpr5Modules

Simplified simulation of expression data

unsignedAdjacency

Calculation of unsigned adjacency

standardScreeningNumericTrait

Standard screening for numeric traits

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Space-less paste

Colors this library uses for labeling modules.

simulateSmallLayer

Simulate small modules

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

Opens a graphics window with specified dimensions

transposeBigData

Transpose a big matrix or data frame

Scatterplot with density

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

standardScreeningBinaryTrait

Standard screening for binatry traits

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

Topological overlap for a subset of the whole set of genes

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

votingLinearPredictor

Voting linear predictor

Boxplot annotated by a Kruskal-Wallis p-value

Topological overlap for a subset of a whole set of genes

verboseScatterplot

Scatterplot annotated by regression line and p-value

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

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

Stem Cell-Related Genes with Corresponding Gene Markers

Generalized Topological Overlap Measure

Blood Cell Types with Corresponding Gene Markers

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

Brain-Related Categories with Corresponding Gene Markers

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

Various basic operations on BlockwiseData objects.

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers