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WGCNA (version 1.71)

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

13,606

Version

1.71

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

April 22nd, 2022

Functions in WGCNA (1.71)

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

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

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

Various basic operations on BlockwiseData objects.

Blood Cell Types with Corresponding Gene Markers

Generalized Topological Overlap Measure

Stem Cell-Related Genes with Corresponding Gene Markers

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

Brain-Related Categories with Corresponding Gene Markers

Graphical representation of the Topological Overlap Matrix

Add grid lines to an existing plot.

Topological overlap matrix similarity and dissimilarity

Weights used in biweight midcovariance

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

Add error bars to a barplot.

Calculation of biweight midcorrelations and associated p-values

accuracyMeasures

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

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

adjacency.polyReg

Adjacency matrix based on polynomial regression

Estimate the true trait underlying a list of surrogate markers.

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

TOMsimilarityFromExpr

Topological overlap matrix

Calculate network adjacency

Add trait information to multi-set module eigengene structure

Divide tasks among workers

Align expression data with given vector

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

automaticNetworkScreening

One-step automatic network gene screening

Biweight Midcorrelation

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

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

branchSplit.dissim

Branch split based on dissimilarity.

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

Select one representative row per group

Blue-white-red color sequence

blockwiseConsensusModules

Find consensus modules across several datasets.

chooseTopHubInEachModule

Chooses the top hub gene in each module

Clustering coefficient calculation

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

blockwiseModules

Automatic network construction and module detection

Co-clustering measure of cluster preservation between two clusterings

collapseRowsUsingKME

Selects one representative row per group based on kME

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

consensusRepresentatives

Consensus selection of group representatives

Consensus network (topological overlap).

factorizeNonNumericColumns

Turn non-numeric columns into factors

fixDataStructure

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

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Iterative garbage collection.

Break long character strings into multiple lines

Fast calculations of Pearson correlation.

coClustering.permutationTest

Permutation test for co-clustering

branchSplitFromStabilityLabels

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

Calculation of correlations and associated p-values

Check adjacency matrix

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

Check structure and retrieve sizes of a group of datasets.

chooseOneHubInEachModule

Chooses a single hub gene in each module

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

corPredictionSuccess

Qunatification of success of gene screening

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

Calculate individual correlation network matrices

Inline display of progress

Inline display of progress

corPvalueFisher

Fisher's asymptotic p-value for correlation

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

Constant-height tree cut

consensusProjectiveKMeans

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

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

consensusTreeInputs

Get all elementary inputs in a consensus tree

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Filter samples with too many missing entries

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

Convert numerical labels to colors.

consensusCalculation

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

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

dynamicMergeCut

Threshold for module merging

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

matrixToNetwork

Construct a network from a matrix

mergeCloseModules

Merge close modules in gene expression data

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

Barplot with text or color labels.

Label scatterplot points

corPvalueStudent

Student asymptotic p-value for correlation

correlationPreservation

Preservation of eigengene correlations

intramodularConnectivity

Calculation of intramodular connectivity

exportNetworkToCytoscape

Export network to Cytoscape

exportNetworkToVisANT

Export network data in format readable by VisANT

Green-black-red color sequence

Green-white-red color sequence

Determine whether the supplied object is a valid multiData structure

multiData.eigengeneSignificance

Eigengene significance across multiple sets

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

goodSamplesGenesMS

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

hierarchicalConsensusCalculation

Hierarchical consensus calculation

Meta-analysis Z statistic

Meta-analysis of binary and continuous variables

newBlockInformation

Create a list holding information about dividing data into blocks

networkScreeningGS

Network gene screening with an external gene significance measure

hierarchicalConsensusKME

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

cutreeStaticColor

Constant height tree cut using color labels

Show colors used to label modules

Fixed-height cut of a dendrogram.

Filter samples with too many missing entries across multiple data sets

modulePreservation

Calculation of module preservation statistics

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

Filter genes with too many missing entries

mtd.setColnames

Get and set column names in a multiData structure.

nearestNeighborConnectivityMS

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

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

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

Filter genes with too many missing entries across multiple sets

Put close eigenvectors next to each other

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

Prepend a comma to a non-empty string

Pad numbers with leading zeros to specified total width

plotNetworkHeatmap

Network heatmap plot

Plot multiple histograms in a single plot

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

networkConcepts

Calculations of network concepts

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

networkScreening

Identification of genes related to a trait

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Red-white-green color sequence

Number of present data entries.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

Set attributes on each component of a multiData structure

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

Simulate a gene co-expression module

relativeCorPredictionSuccess

Compare prediction success

sampledBlockwiseModules

Blockwise module identification in sampled data

Red and Green Color Specification

simulateEigengeneNetwork

Simulate eigengene network from a causal model

standardScreeningNumericTrait

Standard screening for numeric traits

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

Boxplot annotated by a Kruskal-Wallis p-value

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

newConsensusTree

Create a new consensus tree

newCorrelationOptions

Creates a list of correlation options.

hubGeneSignificance

Hubgene significance

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

Impute missing data separately in each module

keepCommonProbes

Keep probes that are shared among given data sets

moduleMergeUsingKME

Merge modules and reassign genes using kME.

moduleEigengenes

Calculate module eigengenes.

Color representation for a numeric variable

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

proportionsInAdmixture

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

Signed eigengene-based connectivity

propVarExplained

Proportion of variance explained by eigengenes.

Replace missing values with a constant.

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

simulateDatExpr

Simulation of expression data

simulateDatExpr5Modules

Simplified simulation of expression data

Apply a function to each set in a multiData structure.

Apply a function to elements of given multiData structures.

Scatterplot with density

Number of sets in a multi-set variable

nearestCentroidPredictor

Nearest centroid predictor

Space-less paste

softConnectivity

Calculates connectivity of a weighted network.

userListEnrichment

Measure enrichment between inputted and user-defined lists

overlapTableUsingKME

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

Calculate overlap of modules

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

Produce a labeled heatmap plot

newNetworkOptions

Create a list of network construction arguments (options).

Red and Green Color Image of Correlation Matrix

Topological overlap for a subset of the whole set of genes

plotDendroAndColors

Dendrogram plot with color annotation of objects

normalizeLabels

Transform numerical labels into normal order.

populationMeansInAdmixture

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

Parallel quantile, median, mean

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

plotColorUnderTree

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

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

Removes the grey eigengene from a given collection of eigengenes.

simulateMultiExpr

Simulate multi-set expression data

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

removePrincipalComponents

Remove leading principal components from data

Red and Green Color Image of Data Matrix

simulateSmallLayer

Simulate small modules

Topological overlap for a subset of a whole set of genes

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

pruneConsensusModules

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

setCorrelationPreservation

Summary correlation preservation measure

sizeRestrictedClusterMerge

Cluter merging with size restrictions

lowerTri2matrix

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

Shorten given character strings by truncating at a suitable separator.

Opens a graphics window with specified dimensions

preservationNetworkConnectivity

Network preservation calculations

Relabel module labels to best match the given reference labels

projectiveKMeans

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

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

plotModuleSignificance

Barplot of module significance

Round numeric columns to given significant digits.

signumAdjacencyFunction

Hard-thresholding adjacency function

transposeBigData

Transpose a big matrix or data frame

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Colors this library uses for labeling modules.

unsignedAdjacency

Calculation of unsigned adjacency

votingLinearPredictor

Voting linear predictor

Subset rows and columns in a multiData structure

verboseScatterplot

Scatterplot annotated by regression line and p-value

standardScreeningBinaryTrait

Standard screening for binatry traits

Create a multiData structure.

Calculate module eigengenes.

Union and intersection of multiple sets

newBlockwiseData

Create, merge and expand BlockwiseData objects

newConsensusOptions

Create a list holding consensus calculation options.

plotEigengeneNetworks

Eigengene network plot

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

Pairwise scatterplots of eigengenes

Rand index of two partitions

qvalue.restricted

qvalue convenience wrapper

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

Standard error of the mean of a given vector.

simpleConsensusCalculation

Simple calculation of a single consenus

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

Visual check of scale-free topology

stratifiedBarplot

Bar plots of data across two splitting parameters

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

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