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WGCNA (version 1.60)

Weighted Correlation Network Analysis

Description

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. 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

16,020

Version

1.60

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

July 10th, 2017

Functions in WGCNA (1.60)

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

Various basic operations on BlockwiseData objects.

Blood Cell Types with Corresponding Gene Markers

Brain-Related Categories with Corresponding Gene Markers

Generalized Topological Overlap Measure

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

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

Stem Cell-Related Genes with Corresponding Gene Markers

Graphical representation of the Topological Overlap Matrix

Topological overlap matrix similarity and dissimilarity

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

Add trait information to multi-set module eigengene structure

Divide tasks among workers

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

branchSplit.dissim

Branch split based on dissimilarity.

branchSplitFromStabilityLabels

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

Add error bars to a barplot.

Add grid lines to an existing plot.

Biweight Midcorrelation

Calculation of biweight midcorrelations and associated p-values

coClustering.permutationTest

Permutation test for co-clustering

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

consensusProjectiveKMeans

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

consensusRepresentatives

Consensus selection of group representatives

Weighted Gene Co-Expression Network Analysis

accuracyMeasures

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

automaticNetworkScreening

One-step automatic network gene screening

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

corPvalueFisher

Fisher's asymptotic p-value for correlation

corPvalueStudent

Student asymptotic p-value for correlation

Show colors used to label modules

TOMsimilarityFromExpr

Topological overlap matrix

Estimate the true trait underlying a list of surrogate markers.

adjacency.polyReg

Adjacency matrix based on polynomial regression

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

chooseOneHubInEachModule

Chooses a single hub gene in each module

Weights used in biweight midcovariance

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

Clustering coefficient calculation

Co-clustering measure of cluster preservation between two clusterings

Calculate network adjacency

dynamicMergeCut

Threshold for module merging

Green-black-red color sequence

Green-white-red color sequence

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

chooseTopHubInEachModule

Chooses the top hub gene in each module

Iterative garbage collection.

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

correlationPreservation

Preservation of eigengene correlations

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

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

lowerTri2matrix

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

Meta-analysis Z statistic

blockwiseConsensusModules

Find consensus modules across several datasets.

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

Select one representative row per group

collapseRowsUsingKME

Selects one representative row per group based on kME

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

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

Calculate module eigengenes.

newBlockInformation

Create a list holding information about dividing data into blocks

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

exportNetworkToVisANT

Export network data in format readable by VisANT

fixDataStructure

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

newBlockwiseData

Create, merge and expand BlockwiseData objects

newConsensusOptions

Create a list holding consensus calculation options.

newConsensusTree

Create a new consensus tree

overlapTableUsingKME

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

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

Consensus network (topological overlap).

Fast calculations of Pearson correlation.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

Parallel quantile, median, mean

Prepend a comma to a non-empty string

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

Filter genes with too many missing entries

Filter genes with too many missing entries across multiple sets

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

Determine whether the supplied object is a valid multiData structure

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Align expression data with given vector

blockwiseModules

Automatic network construction and module detection

Blue-white-red color sequence

Check adjacency matrix

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

Visual check of scale-free topology

simpleConsensusCalculation

Simple calculation of a single consenus

Check structure and retrieve sizes of a group of datasets.

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

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

Calculation of correlations and associated p-values

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

standardScreeningNumericTrait

Standard screening for numeric traits

Standard error of the mean of a given vector.

unsignedAdjacency

Calculation of unsigned adjacency

corPredictionSuccess

Qunatification of success of gene screening

Break long character strings into multiple lines

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

hierarchicalConsensusCalculation

Hierarchical consensus calculation

hierarchicalConsensusKME

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

keepCommonProbes

Keep probes that are shared among given data sets

Label scatterplot points

Barplot with text or color labels.

Convert numerical labels to colors.

moduleMergeUsingKME

Merge modules and reassign genes using kME.

Fixed-height cut of a dendrogram.

Create a multiData structure.

userListEnrichment

Measure enrichment between inputted and user-defined lists

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

consensusCalculation

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

Constant-height tree cut

cutreeStaticColor

Constant height tree cut using color labels

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

Filter samples with too many missing entries

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Inline display of progress

Inline display of progress

intramodularConnectivity

Calculation of intramodular connectivity

multiData.eigengeneSignificance

Eigengene significance across multiple sets

nearestNeighborConnectivityMS

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

networkConcepts

Calculations of network concepts

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

Relabel module labels to best match the given reference labels

matrixToNetwork

Construct a network from a matrix

modulePreservation

Calculation of module preservation statistics

Produce a labeled heatmap plot

Apply a function to elements of given multiData structures.

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

exportNetworkToCytoscape

Export network to Cytoscape

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

hubGeneSignificance

Hubgene significance

Apply a function to each set in a multiData structure.

Set attributes on each component of a multiData structure

mtd.setColnames

Get and set column names in a multiData structure.

nearestCentroidPredictor

Nearest centroid predictor

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

Put close eigenvectors next to each other

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

plotNetworkHeatmap

Network heatmap plot

Subset rows and columns in a multiData structure

Number of present data entries.

Number of sets in a multi-set variable

normalizeLabels

Transform numerical labels into normal order.

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

Calculate overlap of modules

plotColorUnderTree

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

Red and Green Color Image of Correlation Matrix

Pad numbers with leading zeros to specified total width

preservationNetworkConnectivity

Network preservation calculations

Calculate individual correlation network matrices

mergeCloseModules

Merge close modules in gene expression data

Meta-analysis of binary and continuous variables

populationMeansInAdmixture

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

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Red-white-green color sequence

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

moduleEigengenes

Calculate module eigengenes.

Union and intersection of multiple sets

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

Color representation for a numeric variable

Pairwise scatterplots of eigengenes

Red and Green Color Image of Data Matrix

plotModuleSignificance

Barplot of module significance

Plot multiple histograms in a single plot

proportionsInAdmixture

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

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

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

relativeCorPredictionSuccess

Compare prediction success

Removes the grey eigengene from a given collection of eigengenes.

sampledBlockwiseModules

Blockwise module identification in sampled data

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

setCorrelationPreservation

Summary correlation preservation measure

Signed eigengene-based connectivity

signumAdjacencyFunction

Hard-thresholding adjacency function

Opens a graphics window with specified dimensions

softConnectivity

Calculates connectivity of a weighted network.

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

Boxplot annotated by a Kruskal-Wallis p-value

Scatterplot with density

Red and Green Color Specification

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Simulate a gene co-expression module

simulateMultiExpr

Simulate multi-set expression data

simulateDatExpr

Simulation of expression data

simulateDatExpr5Modules

Simplified simulation of expression data

standardScreeningBinaryTrait

Standard screening for binatry traits

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

verboseScatterplot

Scatterplot annotated by regression line and p-value

networkScreening

Identification of genes related to a trait

networkScreeningGS

Network gene screening with an external gene significance measure

newCorrelationOptions

Creates a list of correlation options.

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

transposeBigData

Transpose a big matrix or data frame

simulateSmallLayer

Simulate small modules

Topological overlap for a subset of the whole set of genes

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

votingLinearPredictor

Voting linear predictor

newNetworkOptions

Create a list of network construction arguments (options).

plotDendroAndColors

Dendrogram plot with color annotation of objects

plotEigengeneNetworks

Eigengene network plot

projectiveKMeans

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

propVarExplained

Proportion of variance explained by eigengenes.

qvalue.restricted

qvalue convenience wrapper

Rand index of two partitions

removePrincipalComponents

Remove leading principal components from data

Replace missing values with a constant.

Shorten given character strings by truncating at a suitable separator.

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Space-less paste

Colors this library uses for labeling modules.

stratifiedBarplot

Bar plots of data across two splitting parameters

Topological overlap for a subset of a whole set of genes