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WGCNA (version 1.68)

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

1.68

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

May 23rd, 2019

Functions in WGCNA (1.68)

Generalized Topological Overlap Measure

TOMsimilarityFromExpr

Topological overlap matrix

Align expression data with given vector

Divide tasks among workers

Estimate the true trait underlying a list of surrogate markers.

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

Check adjacency matrix

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

consensusProjectiveKMeans

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

consensusTreeInputs

Get all elementary inputs in a consensus tree

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

factorizeNonNumericColumns

Turn non-numeric columns into factors

fixDataStructure

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

Break long character strings into multiple lines

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

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

hierarchicalConsensusCalculation

Hierarchical consensus calculation

Stem Cell-Related Genes with Corresponding Gene Markers

hierarchicalConsensusKME

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

Label scatterplot points

Graphical representation of the Topological Overlap Matrix

Barplot with text or color labels.

Topological overlap matrix similarity and dissimilarity

Meta-analysis of binary and continuous variables

Meta-analysis Z statistic

Calculation of biweight midcorrelations and associated p-values

Weights used in biweight midcovariance

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

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

chooseTopHubInEachModule

Chooses the top hub gene in each module

Clustering coefficient calculation

collapseRowsUsingKME

Selects one representative row per group based on kME

Iterative garbage collection.

Set attributes on each component of a multiData structure

Calculate module eigengenes.

consensusRepresentatives

Consensus selection of group representatives

Consensus network (topological overlap).

Union and intersection of multiple sets

exportNetworkToCytoscape

Export network to Cytoscape

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

exportNetworkToVisANT

Export network data in format readable by VisANT

newNetworkOptions

Create a list of network construction arguments (options).

Green-black-red color sequence

Green-white-red color sequence

normalizeLabels

Transform numerical labels into normal order.

Blood Cell Types with Corresponding Gene Markers

hubGeneSignificance

Hubgene significance

Impute missing data separately in each module

Convert numerical labels to colors.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

accuracyMeasures

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

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

Brain-Related Categories with Corresponding Gene Markers

Add error bars to a barplot.

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Red and Green Color Image of Data Matrix

projectiveKMeans

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

plotModuleSignificance

Barplot of module significance

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

preservationNetworkConnectivity

Network preservation calculations

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

Add trait information to multi-set module eigengene structure

Calculate network adjacency

adjacency.polyReg

Adjacency matrix based on polynomial regression

automaticNetworkScreening

One-step automatic network gene screening

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

Fixed-height cut of a dendrogram.

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

modulePreservation

Calculation of module preservation statistics

branchSplit.dissim

Branch split based on dissimilarity.

Check structure and retrieve sizes of a group of datasets.

chooseOneHubInEachModule

Chooses a single hub gene in each module

Red-white-green color sequence

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

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

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

corPvalueStudent

Student asymptotic p-value for correlation

Various basic operations on BlockwiseData objects.

correlationPreservation

Preservation of eigengene correlations

Add grid lines to an existing plot.

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

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

Constant-height tree cut

blockwiseConsensusModules

Find consensus modules across several datasets.

relativeCorPredictionSuccess

Compare prediction success

Filter samples with too many missing entries

Co-clustering measure of cluster preservation between two clusterings

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Calculate individual correlation network matrices

Number of sets in a multi-set variable

nearestCentroidPredictor

Nearest centroid predictor

newConsensusTree

Create a new consensus tree

newCorrelationOptions

Creates a list of correlation options.

Biweight Midcorrelation

Visual check of scale-free topology

Blue-white-red color sequence

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

coClustering.permutationTest

Permutation test for co-clustering

Color representation for a numeric variable

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

Inline display of progress

Inline display of progress

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Select one representative row per group

intramodularConnectivity

Calculation of intramodular connectivity

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

consensusCalculation

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

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

corPredictionSuccess

Qunatification of success of gene screening

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

plotEigengeneNetworks

Eigengene network plot

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

Determine whether the supplied object is a valid multiData structure

corPvalueFisher

Fisher's asymptotic p-value for correlation

lowerTri2matrix

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

Fast calculations of Pearson correlation.

Pairwise scatterplots of eigengenes

Relabel module labels to best match the given reference labels

propVarExplained

Proportion of variance explained by eigengenes.

Calculation of correlations and associated p-values

dynamicMergeCut

Threshold for module merging

Apply a function to each set in a multiData structure.

proportionsInAdmixture

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

Apply a function to elements of given multiData structures.

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

cutreeStaticColor

Constant height tree cut using color labels

Show colors used to label modules

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

Filter genes with too many missing entries

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 genes with too many missing entries across multiple sets

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Filter samples with too many missing entries across multiple data sets

nearestNeighborConnectivityMS

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

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

networkScreeningGS

Network gene screening with an external gene significance measure

Simulate a gene co-expression module

newBlockInformation

Create a list holding information about dividing data into blocks

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Produce a labeled heatmap plot

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

Calculate overlap of modules

overlapTableUsingKME

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

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

keepCommonProbes

Keep probes that are shared among given data sets

Standard error of the mean of a given vector.

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

moduleEigengenes

Calculate module eigengenes.

Signed eigengene-based connectivity

matrixToNetwork

Construct a network from a matrix

moduleMergeUsingKME

Merge modules and reassign genes using kME.

stratifiedBarplot

Bar plots of data across two splitting parameters

userListEnrichment

Measure enrichment between inputted and user-defined lists

mergeCloseModules

Merge close modules in gene expression data

mtd.setColnames

Get and set column names in a multiData structure.

populationMeansInAdmixture

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

Topological overlap for a subset of the whole set of genes

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

Parallel quantile, median, mean

multiData.eigengeneSignificance

Eigengene significance across multiple sets

simulateDatExpr

Simulation of expression data

Subset rows and columns in a multiData structure

Create a multiData structure.

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

networkConcepts

Calculations of network concepts

simulateDatExpr5Modules

Simplified simulation of expression data

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

Number of present data entries.

newBlockwiseData

Create, merge and expand BlockwiseData objects

Opens a graphics window with specified dimensions

newConsensusOptions

Create a list holding consensus calculation options.

Put close eigenvectors next to each other

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

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

qvalue.restricted

qvalue convenience wrapper

networkScreening

Identification of genes related to a trait

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

plotColorUnderTree

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

Red and Green Color Image of Correlation Matrix

plotDendroAndColors

Dendrogram plot with color annotation of objects

Plot multiple histograms in a single plot

Replace missing values with a constant.

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

plotNetworkHeatmap

Network heatmap plot

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

sizeRestrictedClusterMerge

Cluter merging with size restrictions

pruneConsensusModules

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

transposeBigData

Transpose a big matrix or data frame

unsignedAdjacency

Calculation of unsigned adjacency

Prepend a comma to a non-empty string

Red and Green Color Specification

Removes the grey eigengene from a given collection of eigengenes.

removePrincipalComponents

Remove leading principal components from data

sampledBlockwiseModules

Blockwise module identification in sampled data

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

Pad numbers with leading zeros to specified total width

simpleConsensusCalculation

Simple calculation of a single consenus

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

softConnectivity

Calculates connectivity of a weighted network.

simulateMultiExpr

Simulate multi-set expression data

simulateSmallLayer

Simulate small modules

Space-less paste

Boxplot annotated by a Kruskal-Wallis p-value

Colors this library uses for labeling modules.

Rand index of two partitions

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

Scatterplot with density

standardScreeningBinaryTrait

Standard screening for binatry traits

Topological overlap for a subset of a whole set of genes

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

verboseScatterplot

Scatterplot annotated by regression line and p-value

setCorrelationPreservation

Summary correlation preservation measure

votingLinearPredictor

Voting linear predictor

Shorten given character strings by truncating at a suitable separator.

Round numeric columns to given significant digits.

signumAdjacencyFunction

Hard-thresholding adjacency function

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

standardScreeningNumericTrait

Standard screening for numeric traits

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

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

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers