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WGCNA (version 1.66)

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

13,054

Version

1.66

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

October 24th, 2018

Functions in WGCNA (1.66)

Divide tasks among workers

Brain-Related Categories with Corresponding Gene Markers

Graphical representation of the Topological Overlap Matrix

Blood Cell Types with Corresponding Gene Markers

Topological overlap matrix similarity and dissimilarity

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

automaticNetworkScreening

One-step automatic network gene screening

branchSplit.dissim

Branch split based on dissimilarity.

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

Check structure and retrieve sizes of a group of datasets.

blockwiseConsensusModules

Find consensus modules across several datasets.

Generalized Topological Overlap Measure

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

branchSplitFromStabilityLabels

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

Add grid lines to an existing plot.

chooseOneHubInEachModule

Chooses a single hub gene in each module

Check adjacency matrix

consensusCalculation

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

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

collapseRowsUsingKME

Selects one representative row per group based on kME

Various basic operations on BlockwiseData objects.

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

Add trait information to multi-set module eigengene structure

Calculate network adjacency

Iterative garbage collection.

Fast calculations of Pearson correlation.

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

adjacency.polyReg

Adjacency matrix based on polynomial regression

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

Calculation of correlations and associated p-values

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

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

corPvalueStudent

Student asymptotic p-value for correlation

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

correlationPreservation

Preservation of eigengene correlations

Constant-height tree cut

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

Select one representative row per group

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

Break long character strings into multiple lines

dynamicMergeCut

Threshold for module merging

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

consensusRepresentatives

Consensus selection of group representatives

blockwiseModules

Automatic network construction and module detection

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

hubGeneSignificance

Hubgene significance

Impute missing data separately in each module

Co-clustering measure of cluster preservation between two clusterings

accuracyMeasures

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

Consensus network (topological overlap).

Label scatterplot points

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Convert numerical labels to colors.

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

Barplot with text or color labels.

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

coClustering.permutationTest

Permutation test for co-clustering

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

Add error bars to a barplot.

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

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

matrixToNetwork

Construct a network from a matrix

Fixed-height cut of a dendrogram.

mergeCloseModules

Merge close modules in gene expression data

modulePreservation

Calculation of module preservation statistics

Biweight Midcorrelation

TOMsimilarityFromExpr

Topological overlap matrix

goodSamplesGenesMS

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

Number of sets in a multi-set variable

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

consensusProjectiveKMeans

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

nearestCentroidPredictor

Nearest centroid predictor

Calculate module eigengenes.

Estimate the true trait underlying a list of surrogate markers.

factorizeNonNumericColumns

Turn non-numeric columns into factors

corPredictionSuccess

Qunatification of success of gene screening

Filter samples with too many missing entries across multiple data sets

corPvalueFisher

Fisher's asymptotic p-value for correlation

newBlockwiseData

Create, merge and expand BlockwiseData objects

fixDataStructure

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

Union and intersection of multiple sets

Filter samples with too many missing entries

exportNetworkToCytoscape

Export network to Cytoscape

newConsensusOptions

Create a list holding consensus calculation options.

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

intramodularConnectivity

Calculation of intramodular connectivity

Determine whether the supplied object is a valid multiData structure

exportNetworkToVisANT

Export network data in format readable by VisANT

Color representation for a numeric variable

Green-black-red color sequence

Calculation of biweight midcorrelations and associated p-values

networkScreeningGS

Network gene screening with an external gene significance measure

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

newBlockInformation

Create a list holding information about dividing data into blocks

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

Green-white-red color sequence

Calculate overlap of modules

Calculate individual correlation network matrices

Red and Green Color Image of Correlation Matrix

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

keepCommonProbes

Keep probes that are shared among given data sets

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Weights used in biweight midcovariance

Blue-white-red color sequence

lowerTri2matrix

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

overlapTableUsingKME

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

populationMeansInAdmixture

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

Relabel module labels to best match the given reference labels

plotDendroAndColors

Dendrogram plot with color annotation of objects

Inline display of progress

Inline display of progress

Apply a function to each set in a multiData structure.

propVarExplained

Proportion of variance explained by eigengenes.

chooseTopHubInEachModule

Chooses the top hub gene in each module

proportionsInAdmixture

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

moduleEigengenes

Calculate module eigengenes.

Clustering coefficient calculation

moduleMergeUsingKME

Merge modules and reassign genes using kME.

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

Subset rows and columns in a multiData structure

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

Create a multiData structure.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

Set attributes on each component of a multiData structure

Red-white-green color sequence

consensusTreeInputs

Get all elementary inputs in a consensus tree

relativeCorPredictionSuccess

Compare prediction success

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

Number of present data entries.

Parallel quantile, median, mean

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

pruneConsensusModules

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

Removes the grey eigengene from a given collection of eigengenes.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

Apply a function to elements of given multiData structures.

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

cutreeStaticColor

Constant height tree cut using color labels

Red and Green Color Specification

Show colors used to label modules

sampledBlockwiseModules

Blockwise module identification in sampled data

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

simulateMultiExpr

Simulate multi-set expression data

removePrincipalComponents

Remove leading principal components from data

simulateSmallLayer

Simulate small modules

standardScreeningBinaryTrait

Standard screening for binatry traits

newConsensusTree

Create a new consensus tree

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

unsignedAdjacency

Calculation of unsigned adjacency

Filter genes with too many missing entries

userListEnrichment

Measure enrichment between inputted and user-defined lists

newCorrelationOptions

Creates a list of correlation options.

Filter genes with too many missing entries across multiple sets

Put close eigenvectors next to each other

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

simpleConsensusCalculation

Simple calculation of a single consenus

hierarchicalConsensusCalculation

Hierarchical consensus calculation

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

plotEigengeneNetworks

Eigengene network plot

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

plotModuleSignificance

Barplot of module significance

Red and Green Color Image of Data Matrix

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

hierarchicalConsensusKME

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

networkConcepts

Calculations of network concepts

networkScreening

Identification of genes related to a trait

Produce a labeled heatmap plot

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

Pairwise scatterplots of eigengenes

Meta-analysis of binary and continuous variables

Prepend a comma to a non-empty string

Pad numbers with leading zeros to specified total width

standardScreeningNumericTrait

Standard screening for numeric traits

mtd.setColnames

Get and set column names in a multiData structure.

Meta-analysis Z statistic

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

Standard error of the mean of a given vector.

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Visual check of scale-free topology

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

qvalue.restricted

qvalue convenience wrapper

transposeBigData

Transpose a big matrix or data frame

Rand index of two partitions

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

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

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

nearestNeighborConnectivityMS

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

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

Round numeric columns to given significant digits.

setCorrelationPreservation

Summary correlation preservation measure

signumAdjacencyFunction

Hard-thresholding adjacency function

stratifiedBarplot

Bar plots of data across two splitting parameters

Replace missing values with a constant.

Shorten given character strings by truncating at a suitable separator.

simulateDatExpr

Simulation of expression data

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

Topological overlap for a subset of a whole set of genes

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

simulateDatExpr5Modules

Simplified simulation of expression data

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

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

Opens a graphics window with specified dimensions

softConnectivity

Calculates connectivity of a weighted network.

Boxplot annotated by a Kruskal-Wallis p-value

newNetworkOptions

Create a list of network construction arguments (options).

Topological overlap for a subset of the whole set of genes

normalizeLabels

Transform numerical labels into normal order.

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

plotColorUnderTree

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

Plot multiple histograms in a single plot

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

plotNetworkHeatmap

Network heatmap plot

preservationNetworkConnectivity

Network preservation calculations

projectiveKMeans

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

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Signed eigengene-based connectivity

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Simulate a gene co-expression module

Space-less paste

Colors this library uses for labeling modules.

Scatterplot with density

verboseScatterplot

Scatterplot annotated by regression line and p-value

votingLinearPredictor

Voting linear predictor

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

Stem Cell-Related Genes with Corresponding Gene Markers

Align expression data with given vector