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WGCNA (version 1.69)

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

1.69

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

February 28th, 2020

Functions in WGCNA (1.69)

accuracyMeasures

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

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

TOMsimilarityFromExpr

Topological overlap matrix

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Biweight Midcorrelation

Add error bars to a barplot.

Stem Cell-Related Genes with Corresponding Gene Markers

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

Divide tasks among workers

Align expression data with given vector

Add trait information to multi-set module eigengene structure

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

Graphical representation of the Topological Overlap Matrix

Blue-white-red color sequence

Estimate the true trait underlying a list of surrogate markers.

adjacency.polyReg

Adjacency matrix based on polynomial regression

Calculate network adjacency

Topological overlap matrix similarity and dissimilarity

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

Calculation of biweight midcorrelations and associated p-values

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

automaticNetworkScreening

One-step automatic network gene screening

Select one representative row per group

Weights used in biweight midcovariance

chooseOneHubInEachModule

Chooses a single hub gene in each module

Check structure and retrieve sizes of a group of datasets.

blockwiseConsensusModules

Find consensus modules across several datasets.

branchSplitFromStabilityLabels

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

blockwiseModules

Automatic network construction and module detection

branchSplit.dissim

Branch split based on dissimilarity.

collapseRowsUsingKME

Selects one representative row per group based on kME

Iterative garbage collection.

Check adjacency matrix

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

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

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

Co-clustering measure of cluster preservation between two clusterings

consensusCalculation

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

chooseTopHubInEachModule

Chooses the top hub gene in each module

Clustering coefficient calculation

consensusProjectiveKMeans

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

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

corPredictionSuccess

Qunatification of success of gene screening

corPvalueFisher

Fisher's asymptotic p-value for correlation

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

coClustering.permutationTest

Permutation test for co-clustering

Fast calculations of Pearson correlation.

Calculation of correlations and associated p-values

consensusTreeInputs

Get all elementary inputs in a consensus tree

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

cutreeStaticColor

Constant height tree cut using color labels

corPvalueStudent

Student asymptotic p-value for correlation

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

dynamicMergeCut

Threshold for module merging

factorizeNonNumericColumns

Turn non-numeric columns into factors

Filter samples with too many missing entries

fixDataStructure

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

correlationPreservation

Preservation of eigengene correlations

goodSamplesGenes

Iterative filtering of samples and 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 across multiple data sets

Show colors used to label modules

Filter genes with too many missing entries across multiple sets

hierarchicalConsensusCalculation

Hierarchical consensus calculation

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

Filter genes with too many missing entries

hierarchicalConsensusKME

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

Impute missing data separately in each module

hubGeneSignificance

Hubgene significance

exportNetworkToVisANT

Export network data in format readable by VisANT

exportNetworkToCytoscape

Export network to Cytoscape

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

Green-black-red color sequence

Label scatterplot points

Barplot with text or color labels.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

Green-white-red color sequence

consensusRepresentatives

Consensus selection of group representatives

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

intramodularConnectivity

Calculation of intramodular connectivity

Convert numerical labels to colors.

Calculate individual correlation network matrices

Produce a labeled heatmap plot

Inline display of progress

Inline display of progress

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

matrixToNetwork

Construct a network from a matrix

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

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

Relabel module labels to best match the given reference labels

lowerTri2matrix

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

multiData.eigengeneSignificance

Eigengene significance across multiple sets

newConsensusOptions

Create a list holding consensus calculation options.

Meta-analysis of binary and continuous variables

Set attributes on each component of a multiData structure

Consensus network (topological overlap).

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

Apply a function to each set in a multiData structure.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

Meta-analysis Z statistic

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

Red and Green Color Image of Correlation Matrix

Create a multiData structure.

Subset rows and columns in a multiData structure

newBlockwiseData

Create, merge and expand BlockwiseData objects

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Determine whether the supplied object is a valid multiData structure

mergeCloseModules

Merge close modules in gene expression data

Apply a function to elements of given multiData structures.

Number of present data entries.

mtd.setColnames

Get and set column names in a multiData structure.

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

propVarExplained

Proportion of variance explained by eigengenes.

plotDendroAndColors

Dendrogram plot with color annotation of objects

nearestCentroidPredictor

Nearest centroid predictor

Number of sets in a multi-set variable

newBlockInformation

Create a list holding information about dividing data into blocks

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

normalizeLabels

Transform numerical labels into normal order.

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

Color representation for a numeric variable

networkScreeningGS

Network gene screening with an external gene significance measure

newNetworkOptions

Create a list of network construction arguments (options).

proportionsInAdmixture

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

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

removePrincipalComponents

Remove leading principal components from data

Removes the grey eigengene from a given collection of eigengenes.

Replace missing values with a constant.

Pairwise scatterplots of eigengenes

Constant-height tree cut

plotEigengeneNetworks

Eigengene network plot

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

plotColorUnderTree

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

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

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

qvalue.restricted

qvalue convenience wrapper

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

sampledBlockwiseModules

Blockwise module identification in sampled data

Red and Green Color Specification

signumAdjacencyFunction

Hard-thresholding adjacency function

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

nearestNeighborConnectivityMS

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

Round numeric columns to given significant digits.

simpleConsensusCalculation

Simple calculation of a single consenus

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

Break long character strings into multiple lines

newConsensusTree

Create a new consensus tree

Standard error of the mean of a given vector.

populationMeansInAdmixture

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

pruneConsensusModules

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

newCorrelationOptions

Creates a list of correlation options.

stratifiedBarplot

Bar plots of data across two splitting parameters

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

Calculate overlap of modules

Parallel quantile, median, mean

overlapTableUsingKME

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

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Plot multiple histograms in a single plot

Shorten given character strings by truncating at a suitable separator.

keepCommonProbes

Keep probes that are shared among given data sets

setCorrelationPreservation

Summary correlation preservation measure

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

moduleMergeUsingKME

Merge modules and reassign genes using kME.

transposeBigData

Transpose a big matrix or data frame

modulePreservation

Calculation of module preservation statistics

moduleEigengenes

Calculate module eigengenes.

unsignedAdjacency

Calculation of unsigned adjacency

Calculate module eigengenes.

Opens a graphics window with specified dimensions

plotNetworkHeatmap

Network heatmap plot

Simulate a gene co-expression module

Union and intersection of multiple sets

Fixed-height cut of a dendrogram.

preservationNetworkConnectivity

Network preservation calculations

simulateEigengeneNetwork

Simulate eigengene network from a causal model

networkConcepts

Calculations of network concepts

projectiveKMeans

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

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

Rand index of two partitions

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

networkScreening

Identification of genes related to a trait

standardScreeningNumericTrait

Standard screening for numeric traits

Put close eigenvectors next to each other

Red and Green Color Image of Data Matrix

Signed eigengene-based connectivity

softConnectivity

Calculates connectivity of a weighted network.

simulateDatExpr

Simulation of expression data

simulateDatExpr5Modules

Simplified simulation of expression data

Topological overlap for a subset of the whole set of genes

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

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

sizeRestrictedClusterMerge

Cluter merging with size restrictions

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

userListEnrichment

Measure enrichment between inputted and user-defined lists

Space-less paste

Boxplot annotated by a Kruskal-Wallis p-value

plotModuleSignificance

Barplot of module significance

Pad numbers with leading zeros to specified total width

Prepend a comma to a non-empty string

simulateMultiExpr

Simulate multi-set expression data

Red-white-green color sequence

standardScreeningBinaryTrait

Standard screening for binatry traits

Topological overlap for a subset of a whole set of genes

Colors this library uses for labeling modules.

simulateSmallLayer

Simulate small modules

relativeCorPredictionSuccess

Compare prediction success

Visual check of scale-free topology

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

Scatterplot with density

verboseScatterplot

Scatterplot annotated by regression line and p-value

votingLinearPredictor

Voting linear predictor

Blood Cell Types with Corresponding Gene Markers

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

Brain-Related Categories with Corresponding Gene Markers

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

Various basic operations on BlockwiseData objects.

Generalized Topological Overlap Measure

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

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation