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WGCNA (version 1.72-5)

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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Install

install.packages('WGCNA')

Monthly Downloads

13,606

Version

1.72-5

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

December 7th, 2023

Functions in WGCNA (1.72-5)

accuracyMeasures

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

Add trait information to multi-set module eigengene structure

Add grid lines to an existing plot.

TOMsimilarityFromExpr

Topological overlap matrix

Estimate the true trait underlying a list of surrogate markers.

Topological overlap matrix similarity and dissimilarity

Add error bars to a barplot.

Graphical representation of the Topological Overlap Matrix

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

Calculate network adjacency

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

automaticNetworkScreening

One-step automatic network gene screening

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Divide tasks among workers

Biweight Midcorrelation

Weights used in biweight midcovariance

Calculation of biweight midcorrelations and associated p-values

adjacency.polyReg

Adjacency matrix based on polynomial regression

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

Align expression data with given vector

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

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

blockwiseModules

Automatic network construction and module detection

branchSplit.dissim

Branch split based on dissimilarity.

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Blue-white-red color sequence

Select one representative row per group

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

branchSplitFromStabilityLabels

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

blockwiseConsensusModules

Find consensus modules across several datasets.

Check adjacency matrix

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

Check structure and retrieve sizes of a group of datasets.

chooseOneHubInEachModule

Chooses a single hub gene in each module

Clustering coefficient calculation

chooseTopHubInEachModule

Chooses the top hub gene in each module

coClustering.permutationTest

Permutation test for co-clustering

consensusProjectiveKMeans

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

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

Co-clustering measure of cluster preservation between two clusterings

consensusCalculation

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

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

collapseRowsUsingKME

Selects one representative row per group based on kME

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

Iterative garbage collection.

Fast calculations of Pearson correlation.

corPvalueStudent

Student asymptotic p-value for correlation

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

corPredictionSuccess

Qunatification of success of gene screening

Consensus network (topological overlap).

correlationPreservation

Preservation of eigengene correlations

Calculation of correlations and associated p-values

corPvalueFisher

Fisher's asymptotic p-value for correlation

consensusRepresentatives

Consensus selection of group representatives

consensusTreeInputs

Get all elementary inputs in a consensus tree

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

factorizeNonNumericColumns

Turn non-numeric columns into factors

cutreeStaticColor

Constant height tree cut using color labels

fixDataStructure

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

exportNetworkToVisANT

Export network data in format readable by VisANT

dynamicMergeCut

Threshold for module merging

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

Show colors used to label modules

Constant-height tree cut

exportNetworkToCytoscape

Export network to Cytoscape

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

Break long character strings into multiple lines

Filter genes with too many missing entries

goodSamplesGenesMS

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

Filter samples with too many missing entries

Filter genes with too many missing entries across multiple sets

Filter samples with too many missing entries across multiple data sets

Green-black-red color sequence

Green-white-red color sequence

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

hubGeneSignificance

Hubgene significance

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

hierarchicalConsensusCalculation

Hierarchical consensus calculation

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

Calculate individual correlation network matrices

Inline display of progress

Inline display of progress

hierarchicalConsensusKME

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

Impute missing data separately in each module

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

intramodularConnectivity

Calculation of intramodular connectivity

Label scatterplot points

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

Determine whether the supplied object is a valid multiData structure

Produce a labeled heatmap plot

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

keepCommonProbes

Keep probes that are shared among given data sets

Barplot with text or color labels.

Convert numerical labels to colors.

mergeCloseModules

Merge close modules in gene expression data

moduleMergeUsingKME

Merge modules and reassign genes using kME.

matrixToNetwork

Construct a network from a matrix

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

Meta-analysis of binary and continuous variables

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

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

Fixed-height cut of a dendrogram.

Apply a function to each set in a multiData structure.

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

Subset rows and columns in a multiData structure

mtd.setColnames

Get and set column names in a multiData structure.

modulePreservation

Calculation of module preservation statistics

Apply a function to elements of given multiData structures.

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

Set attributes on each component of a multiData structure

Create a multiData structure.

Number of sets in a multi-set variable

nearestNeighborConnectivityMS

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

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

Union and intersection of multiple sets

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

multiData.eigengeneSignificance

Eigengene significance across multiple sets

Calculate module eigengenes.

Number of present data entries.

nearestCentroidPredictor

Nearest centroid predictor

networkConcepts

Calculations of network concepts

newBlockwiseData

Create, merge and expand BlockwiseData objects

newBlockInformation

Create a list holding information about dividing data into blocks

networkScreening

Identification of genes related to a trait

networkScreeningGS

Network gene screening with an external gene significance measure

newConsensusTree

Create a new consensus tree

newConsensusOptions

Create a list holding consensus calculation options.

normalizeLabels

Transform numerical labels into normal order.

newNetworkOptions

Create a list of network construction arguments (options).

Put close eigenvectors next to each other

overlapTableUsingKME

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

Calculate overlap of modules

plotColorUnderTree

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

Color representation for a numeric variable

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

newCorrelationOptions

Creates a list of correlation options.

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Red and Green Color Image of Correlation Matrix

plotEigengeneNetworks

Eigengene network plot

Parallel quantile, median, mean

Plot multiple histograms in a single plot

Pairwise scatterplots of eigengenes

populationMeansInAdmixture

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

Red and Green Color Image of Data Matrix

plotDendroAndColors

Dendrogram plot with color annotation of objects

plotNetworkHeatmap

Network heatmap plot

plotModuleSignificance

Barplot of module significance

qvalue.restricted

qvalue convenience wrapper

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

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

pruneConsensusModules

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

preservationNetworkConnectivity

Network preservation calculations

projectiveKMeans

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

Prepend a comma to a non-empty string

Pad numbers with leading zeros to specified total width

proportionsInAdmixture

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

propVarExplained

Proportion of variance explained by eigengenes.

Red-white-green color sequence

Replace missing values with a constant.

relativeCorPredictionSuccess

Compare prediction success

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

removePrincipalComponents

Remove leading principal components from data

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Removes the grey eigengene from a given collection of eigengenes.

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

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

Rand index of two partitions

Signed eigengene-based connectivity

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

setCorrelationPreservation

Summary correlation preservation measure

Shorten given character strings by truncating at a suitable separator.

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

Red and Green Color Specification

sampledBlockwiseModules

Blockwise module identification in sampled data

Visual check of scale-free topology

simulateDatExpr5Modules

Simplified simulation of expression data

simulateMultiExpr

Simulate multi-set expression data

simulateDatExpr

Simulation of expression data

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

Round numeric columns to given significant digits.

signumAdjacencyFunction

Hard-thresholding adjacency function

simulateSmallLayer

Simulate small modules

standardScreeningNumericTrait

Standard screening for numeric traits

Opens a graphics window with specified dimensions

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

Simulate a gene co-expression module

softConnectivity

Calculates connectivity of a weighted network.

transposeBigData

Transpose a big matrix or data frame

standardScreeningBinaryTrait

Standard screening for binatry traits

Topological overlap for a subset of the whole set of genes

simpleConsensusCalculation

Simple calculation of a single consenus

Space-less paste

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Colors this library uses for labeling modules.

stratifiedBarplot

Bar plots of data across two splitting parameters

sizeRestrictedClusterMerge

Cluter merging with size restrictions

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

userListEnrichment

Measure enrichment between inputted and user-defined lists

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

Standard error of the mean of a given vector.

Topological overlap for a subset of a whole set of genes

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

unsignedAdjacency

Calculation of unsigned adjacency

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

verboseScatterplot

Scatterplot annotated by regression line and p-value

votingLinearPredictor

Voting linear predictor

Boxplot annotated by a Kruskal-Wallis p-value

Scatterplot with density

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

Generalized Topological Overlap Measure

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

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

Stem Cell-Related Genes with Corresponding Gene Markers

Blood Cell Types with Corresponding Gene Markers

Various basic operations on BlockwiseData objects.

Brain-Related Categories with Corresponding Gene Markers

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

BrainRegionMarkers

Gene Markers for Regions of the Human Brain