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WGCNA (version 1.67)

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

1.67

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

April 11th, 2019

Functions in WGCNA (1.67)

Generalized Topological Overlap Measure

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

TOMsimilarityFromExpr

Topological overlap matrix

Estimate the true trait underlying a list of surrogate markers.

Align expression data with given vector

Divide tasks among workers

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

Add grid lines to an existing plot.

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

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

Blood Cell Types with Corresponding Gene Markers

Brain-Related Categories with Corresponding Gene Markers

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

blockwiseConsensusModules

Find consensus modules across several datasets.

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

Graphical representation of the Topological Overlap Matrix

automaticNetworkScreening

One-step automatic network gene screening

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

Blue-white-red color sequence

Various basic operations on BlockwiseData objects.

accuracyMeasures

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

Calculation of biweight midcorrelations and associated p-values

Topological overlap matrix similarity and dissimilarity

Add error bars to a barplot.

Weights used in biweight midcovariance

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

adjacency.polyReg

Adjacency matrix based on polynomial regression

Check structure and retrieve sizes of a group of datasets.

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Biweight Midcorrelation

chooseOneHubInEachModule

Chooses a single hub gene in each module

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

consensusRepresentatives

Consensus selection of group representatives

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

branchSplit.dissim

Branch split based on dissimilarity.

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

blockwiseModules

Automatic network construction and module detection

Co-clustering measure of cluster preservation between two clusterings

consensusProjectiveKMeans

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

branchSplitFromStabilityLabels

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

Consensus network (topological overlap).

Check adjacency matrix

cutreeStaticColor

Constant height tree cut using color labels

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

Show colors used to label modules

Select one representative row per group

collapseRowsUsingKME

Selects one representative row per group based on kME

consensusCalculation

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

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

Iterative garbage collection.

corPvalueStudent

Student asymptotic p-value for correlation

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

coClustering.permutationTest

Permutation test for co-clustering

correlationPreservation

Preservation of eigengene correlations

hubGeneSignificance

Hubgene significance

Stem Cell-Related Genes with Corresponding Gene Markers

Add trait information to multi-set module eigengene structure

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

Impute missing data separately in each module

Calculate network adjacency

intramodularConnectivity

Calculation of intramodular connectivity

exportNetworkToCytoscape

Export network to Cytoscape

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

Fast calculations of Pearson correlation.

consensusTreeInputs

Get all elementary inputs in a consensus tree

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

Determine whether the supplied object is a valid multiData structure

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

Calculation of correlations and associated p-values

matrixToNetwork

Construct a network from a matrix

mergeCloseModules

Merge close modules in gene expression data

factorizeNonNumericColumns

Turn non-numeric columns into factors

fixDataStructure

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

mtd.setColnames

Get and set column names in a multiData structure.

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

dynamicMergeCut

Threshold for module merging

corPredictionSuccess

Qunatification of success of gene screening

Filter genes with too many missing entries

corPvalueFisher

Fisher's asymptotic p-value for correlation

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

exportNetworkToVisANT

Export network data in format readable by VisANT

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

chooseTopHubInEachModule

Chooses the top hub gene in each module

Constant-height tree cut

Filter genes with too many missing entries across multiple sets

Clustering coefficient calculation

Filter samples with too many missing entries

goodSamplesGenesMS

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

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Green-black-red color sequence

Break long character strings into multiple lines

newConsensusTree

Create a new consensus tree

Filter samples with too many missing entries across multiple data sets

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

Calculate individual correlation network matrices

hierarchicalConsensusCalculation

Hierarchical consensus calculation

newCorrelationOptions

Creates a list of correlation options.

Put close eigenvectors next to each other

hierarchicalConsensusKME

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

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

Inline display of progress

Inline display of progress

Produce a labeled heatmap plot

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

plotEigengeneNetworks

Eigengene network plot

lowerTri2matrix

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

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Relabel module labels to best match the given reference labels

Convert numerical labels to colors.

Label scatterplot points

Pairwise scatterplots of eigengenes

Barplot with text or color labels.

Green-white-red color sequence

moduleEigengenes

Calculate module eigengenes.

Apply a function to each set in a multiData structure.

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

moduleMergeUsingKME

Merge modules and reassign genes using kME.

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

qvalue.restricted

qvalue convenience wrapper

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

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

Meta-analysis of binary and continuous variables

Meta-analysis Z statistic

Set attributes on each component of a multiData structure

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

Red-white-green color sequence

Apply a function to elements of given multiData structures.

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

relativeCorPredictionSuccess

Compare prediction success

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

Number of present data entries.

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

Fixed-height cut of a dendrogram.

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

keepCommonProbes

Keep probes that are shared among given data sets

setCorrelationPreservation

Summary correlation preservation measure

nearestNeighborConnectivityMS

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

Subset rows and columns in a multiData structure

Create a multiData structure.

Shorten given character strings by truncating at a suitable separator.

Calculate module eigengenes.

Union and intersection of multiple sets

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

Round numeric columns to given significant digits.

modulePreservation

Calculation of module preservation statistics

networkScreeningGS

Network gene screening with an external gene significance measure

Number of sets in a multi-set variable

newNetworkOptions

Create a list of network construction arguments (options).

signumAdjacencyFunction

Hard-thresholding adjacency function

newBlockwiseData

Create, merge and expand BlockwiseData objects

normalizeLabels

Transform numerical labels into normal order.

newConsensusOptions

Create a list holding consensus calculation options.

plotColorUnderTree

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

Colors this library uses for labeling modules.

standardScreeningBinaryTrait

Standard screening for binatry traits

nearestCentroidPredictor

Nearest centroid predictor

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

newBlockInformation

Create a list holding information about dividing data into blocks

networkConcepts

Calculations of network concepts

Color representation for a numeric variable

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Calculate overlap of modules

networkScreening

Identification of genes related to a trait

Topological overlap for a subset of a whole set of genes

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

Plot multiple histograms in a single plot

propVarExplained

Proportion of variance explained by eigengenes.

overlapTableUsingKME

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

Red and Green Color Image of Correlation Matrix

plotNetworkHeatmap

Network heatmap plot

Red and Green Color Image of Data Matrix

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

populationMeansInAdmixture

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

plotModuleSignificance

Barplot of module significance

proportionsInAdmixture

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

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

Parallel quantile, median, mean

plotDendroAndColors

Dendrogram plot with color annotation of objects

Red and Green Color Specification

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

Standard error of the mean of a given vector.

sampledBlockwiseModules

Blockwise module identification in sampled data

verboseScatterplot

Scatterplot annotated by regression line and p-value

stratifiedBarplot

Bar plots of data across two splitting parameters

Boxplot annotated by a Kruskal-Wallis p-value

pruneConsensusModules

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

votingLinearPredictor

Voting linear predictor

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Rand index of two partitions

preservationNetworkConnectivity

Network preservation calculations

Scatterplot with density

projectiveKMeans

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

Visual check of scale-free topology

Removes the grey eigengene from a given collection of eigengenes.

removePrincipalComponents

Remove leading principal components from data

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

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

Prepend a comma to a non-empty string

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

simulateMultiExpr

Simulate multi-set expression data

simulateSmallLayer

Simulate small modules

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.

Pad numbers with leading zeros to specified total width

Replace missing values with a constant.

Space-less paste

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

simulateEigengeneNetwork

Simulate eigengene network from a causal model

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Simulate a gene co-expression module

Opens a graphics window with specified dimensions

userListEnrichment

Measure enrichment between inputted and user-defined lists

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

Signed eigengene-based connectivity

Topological overlap for a subset of the whole set of genes

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

simulateDatExpr

Simulation of expression data

sizeRestrictedClusterMerge

Cluter merging with size restrictions

simulateDatExpr5Modules

Simplified simulation of expression data

transposeBigData

Transpose a big matrix or data frame

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

standardScreeningNumericTrait

Standard screening for numeric traits

unsignedAdjacency

Calculation of unsigned adjacency

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers