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WGCNA (version 1.72-1)

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

13,041

Version

1.72-1

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

January 18th, 2023

Functions in WGCNA (1.72-1)

Stem Cell-Related Genes with Corresponding Gene Markers

Blood Cell Types 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

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

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

Brain-Related Categories with Corresponding Gene Markers

Generalized Topological Overlap Measure

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.

TOMsimilarityFromExpr

Topological overlap matrix

Estimate the true trait underlying a list of surrogate markers.

Add grid lines to an existing plot.

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

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

Topological overlap matrix similarity and dissimilarity

Add trait information to multi-set module eigengene structure

Align expression data with given vector

Calculate network adjacency

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

Biweight Midcorrelation

Divide tasks among workers

adjacency.polyReg

Adjacency matrix based on polynomial regression

Calculation of biweight midcorrelations and associated p-values

Weights used in biweight midcovariance

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

Blue-white-red color sequence

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

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

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

branchSplit.dissim

Branch split based on dissimilarity.

chooseOneHubInEachModule

Chooses a single hub gene in each module

Check structure and retrieve sizes of a group of datasets.

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

blockwiseModules

Automatic network construction and module detection

branchSplitFromStabilityLabels

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

Co-clustering measure of cluster preservation between two clusterings

Select one representative row per group

consensusCalculation

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

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

chooseTopHubInEachModule

Chooses the top hub gene in each module

blockwiseConsensusModules

Find consensus modules across several datasets.

coClustering.permutationTest

Permutation test for co-clustering

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

consensusProjectiveKMeans

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

Clustering coefficient calculation

consensusTreeInputs

Get all elementary inputs in a consensus tree

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

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

Check adjacency matrix

collapseRowsUsingKME

Selects one representative row per group based on kME

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

corPvalueStudent

Student asymptotic p-value for correlation

Constant-height tree cut

correlationPreservation

Preservation of eigengene correlations

fixDataStructure

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

factorizeNonNumericColumns

Turn non-numeric columns into factors

Iterative garbage collection.

corPredictionSuccess

Qunatification of success of gene screening

cutreeStaticColor

Constant height tree cut using color labels

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

Fast calculations of Pearson correlation.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

Calculation of correlations and associated p-values

dynamicMergeCut

Threshold for module merging

consensusRepresentatives

Consensus selection of group representatives

Show colors used to label modules

Green-black-red color sequence

corPvalueFisher

Fisher's asymptotic p-value for correlation

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

goodSamplesGenesMS

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

Filter samples with too many missing entries across multiple data sets

Green-white-red color sequence

Filter genes with too many missing entries

exportNetworkToCytoscape

Export network to Cytoscape

Consensus network (topological overlap).

Filter genes with too many missing entries across multiple sets

exportNetworkToVisANT

Export network data in format readable by VisANT

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

Break long character strings into multiple lines

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

hierarchicalConsensusCalculation

Hierarchical consensus calculation

Filter samples with too many missing entries

Inline display of progress

Inline display of progress

Calculate individual correlation network matrices

hubGeneSignificance

Hubgene significance

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

keepCommonProbes

Keep probes that are shared among given data sets

hierarchicalConsensusKME

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

Impute missing data separately in each module

Produce a labeled heatmap plot

Barplot with text or color labels.

Label scatterplot points

intramodularConnectivity

Calculation of intramodular connectivity

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

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

Convert numerical labels to colors.

matrixToNetwork

Construct a network from a matrix

Meta-analysis of binary and continuous variables

Determine whether the supplied object is a valid multiData structure

Meta-analysis Z statistic

lowerTri2matrix

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

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

Relabel module labels to best match the given reference labels

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

mergeCloseModules

Merge close modules in gene expression data

moduleMergeUsingKME

Merge modules and reassign genes using kME.

moduleEigengenes

Calculate module eigengenes.

mtd.setColnames

Get and set column names in a multiData structure.

Fixed-height cut of a dendrogram.

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

Create a multiData structure.

Set attributes on each component of a multiData structure

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

modulePreservation

Calculation of module preservation statistics

Apply a function to each set in a multiData structure.

Subset rows and columns in a multiData structure

Number of sets in a multi-set variable

multiData.eigengeneSignificance

Eigengene significance across multiple sets

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

nearestCentroidPredictor

Nearest centroid predictor

Apply a function to elements of given multiData structures.

Number of present data entries.

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

nearestNeighborConnectivityMS

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

newBlockwiseData

Create, merge and expand BlockwiseData objects

newConsensusOptions

Create a list holding consensus calculation options.

normalizeLabels

Transform numerical labels into normal order.

newNetworkOptions

Create a list of network construction arguments (options).

newBlockInformation

Create a list holding information about dividing data into blocks

networkScreeningGS

Network gene screening with an external gene significance measure

Calculate module eigengenes.

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

Union and intersection of multiple sets

newCorrelationOptions

Creates a list of correlation options.

newConsensusTree

Create a new consensus tree

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

networkScreening

Identification of genes related to a trait

networkConcepts

Calculations of network concepts

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Color representation for a numeric variable

Red and Green Color Image of Correlation Matrix

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

plotColorUnderTree

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

Parallel quantile, median, mean

Calculate overlap of modules

populationMeansInAdmixture

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

plotDendroAndColors

Dendrogram plot with color annotation of objects

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

overlapTableUsingKME

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

plotEigengeneNetworks

Eigengene network plot

Pairwise scatterplots of eigengenes

Red and Green Color Image of Data Matrix

pruneConsensusModules

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

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

plotModuleSignificance

Barplot of module significance

projectiveKMeans

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

preservationNetworkConnectivity

Network preservation calculations

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

Put close eigenvectors next to each other

Prepend a comma to a non-empty string

propVarExplained

Proportion of variance explained by eigengenes.

proportionsInAdmixture

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

Red-white-green color sequence

relativeCorPredictionSuccess

Compare prediction success

plotNetworkHeatmap

Network heatmap plot

Plot multiple histograms in a single plot

qvalue.restricted

qvalue convenience wrapper

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

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Red and Green Color Specification

sampledBlockwiseModules

Blockwise module identification in sampled data

Signed eigengene-based connectivity

Rand index of two partitions

Pad numbers with leading zeros to specified total width

simpleConsensusCalculation

Simple calculation of a single consenus

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

Replace missing values with a constant.

setCorrelationPreservation

Summary correlation preservation measure

Shorten given character strings by truncating at a suitable separator.

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

Round numeric columns to given significant digits.

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

signumAdjacencyFunction

Hard-thresholding adjacency function

Space-less paste

softConnectivity

Calculates connectivity of a weighted network.

Boxplot annotated by a Kruskal-Wallis p-value

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Scatterplot with density

Colors this library uses for labeling modules.

simulateDatExpr

Simulation of expression data

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

Simulate a gene co-expression module

Removes the grey eigengene from a given collection of eigengenes.

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

standardScreeningBinaryTrait

Standard screening for binatry traits

removePrincipalComponents

Remove leading principal components from data

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

Standard error of the mean of a given vector.

Opens a graphics window with specified dimensions

Topological overlap for a subset of a whole set of genes

stratifiedBarplot

Bar plots of data across two splitting parameters

sizeRestrictedClusterMerge

Cluter merging with size restrictions

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

simulateDatExpr5Modules

Simplified simulation of expression data

Visual check of scale-free topology

simulateSmallLayer

Simulate small modules

simulateMultiExpr

Simulate multi-set expression data

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

standardScreeningNumericTrait

Standard screening for numeric traits

transposeBigData

Transpose a big matrix or data frame

unsignedAdjacency

Calculation of unsigned adjacency

Topological overlap for a subset of the whole set of genes

userListEnrichment

Measure enrichment between inputted and user-defined lists

verboseScatterplot

Scatterplot annotated by regression line and p-value

votingLinearPredictor

Voting linear predictor