WGCNA v1.68


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Weighted Correlation Network Analysis

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. 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.

Functions in WGCNA

Name Description
GTOMdist Generalized Topological Overlap Measure
TOMsimilarityFromExpr Topological overlap matrix
alignExpr Align expression data with given vector
allocateJobs Divide tasks among workers
TrueTrait Estimate the true trait underlying a list of surrogate markers.
blockwiseIndividualTOMs Calculation of block-wise topological overlaps
blockwiseModules Automatic network construction and module detection
branchSplitFromStabilityLabels Branch split (dissimilarity) statistics derived from labels determined from a stability study
checkAdjMat Check adjacency matrix
consensusOrderMEs Put close eigenvectors next to each other in several sets.
consensusProjectiveKMeans Consensus projective K-means (pre-)clustering of expression data
consensusTreeInputs Get all elementary inputs in a consensus tree
convertNumericColumnsToNumeric Convert character columns that represent numbers to numeric
factorizeNonNumericColumns Turn non-numeric columns into factors
fixDataStructure Put single-set data into a form useful for multiset calculations.
formatLabels Break long character strings into multiple lines
fundamentalNetworkConcepts Calculation of fundamental network concepts from an adjacency matrix.
PWLists Pathways with Corresponding Gene Markers - Compiled by Mike Palazzolo and Jim Wang from CHDI
hierarchicalConsensusCalculation Hierarchical consensus calculation
SCsLists Stem Cell-Related Genes with Corresponding Gene Markers
hierarchicalConsensusKME Calculation of measures of fuzzy module membership (KME) in hierarchical consensus modules
labelPoints Label scatterplot points
TOMplot Graphical representation of the Topological Overlap Matrix
labeledBarplot Barplot with text or color labels.
TOMsimilarity Topological overlap matrix similarity and dissimilarity
metaAnalysis Meta-analysis of binary and continuous variables
metaZfunction Meta-analysis Z statistic
bicorAndPvalue Calculation of biweight midcorrelations and associated p-values
bicovWeights Weights used in biweight midcovariance
binarizeCategoricalColumns Turn categorical columns into sets of binary indicators
binarizeCategoricalVariable Turn a categorical variable into a set of binary indicators
mtd.rbindSelf Turn a multiData structure into a single matrix or data frame.
chooseTopHubInEachModule Chooses the top hub gene in each module
clusterCoef Clustering coefficient calculation
collapseRowsUsingKME Selects one representative row per group based on kME
collectGarbage Iterative garbage collection.
mtd.setAttr Set attributes on each component of a multiData structure
multiSetMEs Calculate module eigengenes.
consensusRepresentatives Consensus selection of group representatives
consensusTOM Consensus network (topological overlap).
multiUnion Union and intersection of multiple sets
exportNetworkToCytoscape Export network to Cytoscape
BrainRegionMarkers Gene Markers for Regions of the Human Brain
exportNetworkToVisANT Export network data in format readable by VisANT
newNetworkOptions Create a list of network construction arguments (options).
greenBlackRed Green-black-red color sequence
greenWhiteRed Green-white-red color sequence
normalizeLabels Transform numerical labels into normal order.
BloodLists Blood Cell Types with Corresponding Gene Markers
hubGeneSignificance Hubgene significance
imputeByModule Impute missing data separately in each module
labels2colors Convert numerical labels to colors.
pickHardThreshold Analysis of scale free topology for hard-thresholding.
accuracyMeasures Accuracy measures for a 2x2 confusion matrix or for vectors of predicted and observed values.
GOenrichmentAnalysis Calculation of GO enrichment (experimental)
BrainLists Brain-Related Categories with Corresponding Gene Markers
addErrorBars Add error bars to a barplot.
allowWGCNAThreads Allow and disable multi-threading for certain WGCNA calculations
pickSoftThreshold Analysis of scale free topology for soft-thresholding
plotMat Red and Green Color Image of Data Matrix
projectiveKMeans Projective K-means (pre-)clustering of expression data
plotModuleSignificance Barplot of module significance
list2multiData Convert a list to a multiData structure and vice-versa.
preservationNetworkConnectivity Network preservation calculations
minWhichMin Fast joint calculation of row- or column-wise minima and indices of minimum elements
addTraitToMEs Add trait information to multi-set module eigengene structure
adjacency Calculate network adjacency
adjacency.polyReg Adjacency matrix based on polynomial regression
automaticNetworkScreening One-step automatic network gene screening
AFcorMI Prediction of Weighted Mutual Information Adjacency Matrix by Correlation
moduleNumber Fixed-height cut of a dendrogram.
moduleColor.getMEprefix Get the prefix used to label module eigengenes.
modulePreservation Calculation of module preservation statistics
branchSplit Branch split.
branchSplit.dissim Branch split based on dissimilarity.
checkSets Check structure and retrieve sizes of a group of datasets.
chooseOneHubInEachModule Chooses a single hub gene in each module
redWhiteGreen Red-white-green color sequence
conformityBasedNetworkConcepts Calculation of conformity-based network concepts.
blockSize Attempt to calculate an appropriate block size to maximize efficiency of block-wise calcualtions.
adjacency.splineReg Calculate network adjacency based on natural cubic spline regression
conformityDecomposition Conformity and module based decomposition of a network adjacency matrix.
corPvalueStudent Student asymptotic p-value for correlation
BD.getData Various basic operations on BlockwiseData objects.
correlationPreservation Preservation of eigengene correlations
addGrid Add grid lines to an existing plot.
addGuideLines Add vertical ``guide lines'' to a dendrogram plot
automaticNetworkScreeningGS One-step automatic network gene screening with external gene significance
coxRegressionResiduals Deviance- and martingale residuals from a Cox regression model
cutreeStatic Constant-height tree cut
blockwiseConsensusModules Find consensus modules across several datasets.
relativeCorPredictionSuccess Compare prediction success
goodSamples Filter samples with too many missing entries
coClustering Co-clustering measure of cluster preservation between two clusterings
goodSamplesGenes Iterative filtering of samples and genes with too many missing entries
individualTOMs Calculate individual correlation network matrices
nSets Number of sets in a multi-set variable
nearestCentroidPredictor Nearest centroid predictor
newConsensusTree Create a new consensus tree
newCorrelationOptions Creates a list of correlation options.
bicor Biweight Midcorrelation
scaleFreePlot Visual check of scale-free topology
blueWhiteRed Blue-white-red color sequence
selectFewestConsensusMissing Select columns with the lowest consensus number of missing data
coClustering.permutationTest Permutation test for co-clustering
numbers2colors Color representation for a numeric variable
colQuantileC Fast colunm- and row-wise quantile of a matrix.
Inline display of progress Inline display of progress
branchEigengeneDissim Branch dissimilarity based on eigennodes (eigengenes).
collapseRows Select one representative row per group
intramodularConnectivity Calculation of intramodular connectivity
consensusKME Calculate consensus kME (eigengene-based connectivities) across multiple data sets.
consensusCalculation Calculation of a (single) consenus with optional data calibration.
consensusMEDissimilarity Consensus dissimilarity of module eigengenes.
corPredictionSuccess Qunatification of success of gene screening
orderBranchesUsingHubGenes Optimize dendrogram using branch swaps and reflections.
plotEigengeneNetworks Eigengene network plot
consensusDissTOMandTree Consensus clustering based on topological overlap and hierarchical clustering
isMultiData Determine whether the supplied object is a valid multiData structure
corPvalueFisher Fisher's asymptotic p-value for correlation
lowerTri2matrix Reconstruct a symmetric matrix from a distance (lower-triangular) representation
cor Fast calculations of Pearson correlation.
plotMEpairs Pairwise scatterplots of eigengenes
matchLabels Relabel module labels to best match the given reference labels
propVarExplained Proportion of variance explained by eigengenes.
corAndPvalue Calculation of correlations and associated p-values
dynamicMergeCut Threshold for module merging
mtd.apply Apply a function to each set in a multiData structure.
proportionsInAdmixture Estimate the proportion of pure populations in an admixed population based on marker expression values.
mtd.mapply Apply a function to elements of given multiData structures.
nearestNeighborConnectivity Connectivity to a constant number of nearest neighbors
cutreeStaticColor Constant height tree cut using color labels
displayColors Show colors used to label modules
recutBlockwiseTrees Repeat blockwise module detection from pre-calculated data
goodGenes Filter genes with too many missing entries
empiricalBayesLM Empirical Bayes-moderated adjustment for unwanted covariates
goodSamplesGenesMS Iterative filtering of samples and genes with too many missing entries across multiple data sets
goodGenesMS Filter genes with too many missing entries across multiple sets
simulateEigengeneNetwork Simulate eigengene network from a causal model
goodSamplesMS Filter samples with too many missing entries across multiple data sets
nearestNeighborConnectivityMS Connectivity to a constant number of nearest neighbors across multiple data sets
hierarchicalConsensusTOM Calculation of hierarchical consensus topological overlap matrix
networkScreeningGS Network gene screening with an external gene significance measure
simulateModule Simulate a gene co-expression module
newBlockInformation Create a list holding information about dividing data into blocks
recutConsensusTrees Repeat blockwise consensus module detection from pre-calculated data
hierarchicalMergeCloseModules Merge close (similar) hierarchical consensus modules
labeledHeatmap Produce a labeled heatmap plot
hierarchicalConsensusMEDissimilarity Hierarchical consensus calculation of module eigengene dissimilarity
overlapTable Calculate overlap of modules
overlapTableUsingKME Determines significant overlap between modules in two networks based on kME tables.
kMEcomparisonScatterplot Function to plot kME values between two comparable data sets.
hierarchicalConsensusModules Hierarchical consensus network construction and module identification
keepCommonProbes Keep probes that are shared among given data sets
stdErr Standard error of the mean of a given vector.
labeledHeatmap.multiPage Labeled heatmap divided into several separate plots.
sigmoidAdjacencyFunction Sigmoid-type adacency function.
moduleEigengenes Calculate module eigengenes.
signedKME Signed eigengene-based connectivity
matrixToNetwork Construct a network from a matrix
moduleMergeUsingKME Merge modules and reassign genes using kME.
stratifiedBarplot Bar plots of data across two splitting parameters
userListEnrichment Measure enrichment between inputted and user-defined lists
mergeCloseModules Merge close modules in gene expression data
mtd.setColnames Get and set column names in a multiData structure.
populationMeansInAdmixture Estimate the population-specific mean values in an admixed population.
vectorTOM Topological overlap for a subset of the whole set of genes
mtd.simplify If possible, simplify a multiData structure to a 3-dimensional array.
pquantile Parallel quantile, median, mean
multiData.eigengeneSignificance Eigengene significance across multiple sets
simulateDatExpr Simulation of expression data
mtd.subset Subset rows and columns in a multiData structure
multiData Create a multiData structure.
multiGSub Analogs of grep(l) and (g)sub for multiple patterns and relacements
networkConcepts Calculations of network concepts
simulateDatExpr5Modules Simplified simulation of expression data
mutualInfoAdjacency Calculate weighted adjacency matrices based on mutual information
nPresent Number of present data entries.
newBlockwiseData Create, merge and expand BlockwiseData objects
sizeGrWindow Opens a graphics window with specified dimensions
newConsensusOptions Create a list holding consensus calculation options.
orderMEs Put close eigenvectors next to each other
qvalue Estimate the q-values for a given set of p-values
orderMEsByHierarchicalConsensus Order module eigengenes by their hierarchical consensus similarity
qvalue.restricted qvalue convenience wrapper
networkScreening Identification of genes related to a trait
plotClusterTreeSamples Annotated clustering dendrogram of microarray samples
plotColorUnderTree Plot color rows in a given order, for example under a dendrogram
plotCor Red and Green Color Image of Correlation Matrix
plotDendroAndColors Dendrogram plot with color annotation of objects
plotMultiHist Plot multiple histograms in a single plot
replaceMissing Replace missing values with a constant.
returnGeneSetsAsList Return pre-defined gene lists in several biomedical categories.
plotNetworkHeatmap Network heatmap plot
pruneAndMergeConsensusModules Iterative pruning and merging of (hierarchical) consensus modules
sizeRestrictedClusterMerge Cluter merging with size restrictions
pruneConsensusModules Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity
transposeBigData Transpose a big matrix or data frame
unsignedAdjacency Calculation of unsigned adjacency
prepComma Prepend a comma to a non-empty string
rgcolors.func Red and Green Color Specification
removeGreyME Removes the grey eigengene from a given collection of eigengenes.
removePrincipalComponents Remove leading principal components from data
sampledBlockwiseModules Blockwise module identification in sampled data
sampledHierarchicalConsensusModules Hierarchical consensus module identification in sampled data
prependZeros Pad numbers with leading zeros to specified total width
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.
simulateMultiExpr Simulate multi-set expression data
simulateSmallLayer Simulate small modules
spaste Space-less paste
verboseBoxplot Boxplot annotated by a Kruskal-Wallis p-value
standardColors Colors this library uses for labeling modules.
randIndex Rand index of two partitions
rankPvalue Estimate the p-value for ranking consistently high (or low) on multiple lists
verboseIplot Scatterplot with density
standardScreeningBinaryTrait Standard screening for binatry traits
subsetTOM Topological overlap for a subset of a whole set of genes
swapTwoBranches Select, swap, or reflect branches in a dendrogram.
verboseScatterplot Scatterplot annotated by regression line and p-value
setCorrelationPreservation Summary correlation preservation measure
votingLinearPredictor Voting linear predictor
shortenStrings Shorten given character strings by truncating at a suitable separator.
signifNumeric Round numeric columns to given significant digits.
signumAdjacencyFunction Hard-thresholding adjacency function
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
verboseBarplot Barplot with error bars, annotated by Kruskal-Wallis or ANOVA p-value
ImmunePathwayLists Immune Pathways with Corresponding Gene Markers
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