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WGCNA (version 1.63)

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

16,961

Version

1.63

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

February 27th, 2018

Functions in WGCNA (1.63)

Blood Cell Types with Corresponding Gene Markers

Brain-Related Categories with Corresponding Gene Markers

Add error bars to a barplot.

Add grid lines to an existing plot.

Biweight Midcorrelation

Calculation of biweight midcorrelations and associated p-values

branchSplit.dissim

Branch split based on dissimilarity.

branchSplitFromStabilityLabels

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

Check adjacency matrix

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.

Green-black-red color sequence

Green-white-red color sequence

hubGeneSignificance

Hubgene significance

Impute missing data separately in each module

Produce a labeled heatmap plot

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

moduleEigengenes

Calculate module eigengenes.

moduleMergeUsingKME

Merge modules and reassign genes using kME.

Subset rows and columns in a multiData structure

Create a multiData structure.

networkScreeningGS

Network gene screening with an external gene significance measure

newBlockInformation

Create a list holding information about dividing data into blocks

Put close eigenvectors next to each other

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

Red and Green Color Image of Correlation Matrix

plotDendroAndColors

Dendrogram plot with color annotation of objects

preservationNetworkConnectivity

Network preservation calculations

projectiveKMeans

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

Rand index of two partitions

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

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

Visual check of scale-free topology

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

Boxplot annotated by a Kruskal-Wallis p-value

Scatterplot with density

Graphical representation of the Topological Overlap Matrix

Topological overlap matrix similarity and dissimilarity

Divide tasks among workers

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

Clustering coefficient calculation

Co-clustering measure of cluster preservation between two clusterings

consensusProjectiveKMeans

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

consensusRepresentatives

Consensus selection of group representatives

Calculation of correlations and associated p-values

corPredictionSuccess

Qunatification of success of gene screening

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

exportNetworkToCytoscape

Export network to Cytoscape

goodSamplesGenesMS

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

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

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

Filter samples with too many missing entries across multiple data sets

Calculate individual correlation network matrices

Inline display of progress

Inline display of progress

matrixToNetwork

Construct a network from a matrix

mergeCloseModules

Merge close modules in gene expression data

adjacency.polyReg

Adjacency matrix based on polynomial regression

blockwiseConsensusModules

Find consensus modules across several datasets.

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

chooseOneHubInEachModule

Chooses a single hub gene in each module

chooseTopHubInEachModule

Chooses the top hub gene in each module

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

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

mtd.setColnames

Get and set column names in a multiData structure.

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

newConsensusTree

Create a new consensus tree

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

consensusCalculation

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

corPvalueFisher

Fisher's asymptotic p-value for correlation

corPvalueStudent

Student asymptotic p-value for correlation

Filter genes with too many missing entries across multiple sets

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

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

Filter genes with too many missing entries

Stem Cell-Related Genes with Corresponding Gene Markers

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

lowerTri2matrix

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

Relabel module labels to best match the given reference labels

Meta-analysis of binary and continuous variables

Meta-analysis Z statistic

Number of sets in a multi-set variable

nearestCentroidPredictor

Nearest centroid predictor

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

newCorrelationOptions

Creates a list of correlation options.

plotEigengeneNetworks

Eigengene network plot

Pairwise scatterplots of eigengenes

Red and Green Color Image of Data Matrix

plotModuleSignificance

Barplot of module significance

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

setCorrelationPreservation

Summary correlation preservation measure

Shorten given character strings by truncating at a suitable separator.

simulateDatExpr5Modules

Simplified simulation of expression data

Select one representative row per group

collapseRowsUsingKME

Selects one representative row per group based on kME

Constant-height tree cut

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Topological overlap for a subset of a whole set of genes

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

plotColorUnderTree

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

populationMeansInAdmixture

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

Parallel quantile, median, mean

cutreeStaticColor

Constant height tree cut using color labels

Break long character strings into multiple lines

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

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

qvalue.restricted

qvalue convenience wrapper

Removes the grey eigengene from a given collection of eigengenes.

removePrincipalComponents

Remove leading principal components from data

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

simulateDatExpr

Simulation of expression data

intramodularConnectivity

Calculation of intramodular connectivity

Determine whether the supplied object is a valid multiData structure

newBlockwiseData

Create, merge and expand BlockwiseData objects

newConsensusOptions

Create a list holding consensus calculation options.

softConnectivity

Calculates connectivity of a weighted network.

Space-less paste

userListEnrichment

Measure enrichment between inputted and user-defined lists

simulateSmallLayer

Simulate small modules

Opens a graphics window with specified dimensions

Standard error of the mean of a given vector.

stratifiedBarplot

Bar plots of data across two splitting parameters

Topological overlap for a subset of the whole set of genes

verboseScatterplot

Scatterplot annotated by regression line and p-value

votingLinearPredictor

Voting linear predictor

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

TOMsimilarityFromExpr

Topological overlap matrix

Estimate the true trait underlying a list of surrogate markers.

blockwiseModules

Automatic network construction and module detection

Blue-white-red color sequence

Iterative garbage collection.

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

Consensus network (topological overlap).

Fast calculations of Pearson correlation.

Show colors used to label modules

dynamicMergeCut

Threshold for module merging

Convert numerical labels to colors.

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

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

Set attributes on each component of a multiData structure

networkConcepts

Calculations of network concepts

networkScreening

Identification of genes related to a trait

Color representation for a numeric variable

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

Plot multiple histograms in a single plot

plotNetworkHeatmap

Network heatmap plot

Red and Green Color Specification

signumAdjacencyFunction

Hard-thresholding adjacency function

sampledBlockwiseModules

Blockwise module identification in sampled data

simpleConsensusCalculation

Simple calculation of a single consenus

Colors this library uses for labeling modules.

standardScreeningBinaryTrait

Standard screening for binatry traits

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Generalized Topological Overlap Measure

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

Weighted Gene Co-Expression Network Analysis

Align expression data with given vector

Weights used in biweight midcovariance

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

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

correlationPreservation

Preservation of eigengene correlations

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

coClustering.permutationTest

Permutation test for co-clustering

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

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

exportNetworkToVisANT

Export network data in format readable by VisANT

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

Filter samples with too many missing entries

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Label scatterplot points

Barplot with text or color labels.

Apply a function to each set in a multiData structure.

Apply a function to elements of given multiData structures.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

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

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

nearestNeighborConnectivityMS

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

hierarchicalConsensusCalculation

Hierarchical consensus calculation

Calculate overlap of modules

overlapTableUsingKME

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

fixDataStructure

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

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

keepCommonProbes

Keep probes that are shared among given data sets

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

hierarchicalConsensusKME

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

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

pruneConsensusModules

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

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Prepend a comma to a non-empty string

Pad numbers with leading zeros to specified total width

Replace missing values with a constant.

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

Calculate module eigengenes.

Union and intersection of multiple sets

newNetworkOptions

Create a list of network construction arguments (options).

normalizeLabels

Transform numerical labels into normal order.

Signed eigengene-based connectivity

Fixed-height cut of a dendrogram.

modulePreservation

Calculation of module preservation statistics

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

standardScreeningNumericTrait

Standard screening for numeric traits

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

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

Number of present data entries.

propVarExplained

Proportion of variance explained by eigengenes.

proportionsInAdmixture

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

Red-white-green color sequence

relativeCorPredictionSuccess

Compare prediction success

Simulate a gene co-expression module

simulateMultiExpr

Simulate multi-set expression data

transposeBigData

Transpose a big matrix or data frame

unsignedAdjacency

Calculation of unsigned adjacency