# Weighted Correlation Network Analysis

## Documentation for package ‘WGCNA’ version 1.72-1

- DESCRIPTION file.

## Help Pages

A B C D E F G H I K L M N O P Q R S T U V W

## -- A --

accuracyMeasures | Accuracy measures for a 2x2 confusion matrix or for vectors of predicted and observed values.
addBlockToBlockwiseData | Create, merge and expand BlockwiseData objects
addErrorBars | Add error bars to a barplot.
addGrid | Add grid lines to an existing plot.
addGuideLines | Add vertical "guide lines" to a dendrogram plot
addTraitToMEs | Add trait information to multi-set module eigengene structure
adjacency | Calculate network adjacency
adjacency.fromSimilarity | Calculate network adjacency
adjacency.polyReg | Adjacency matrix based on polynomial regression
adjacency.splineReg | Calculate network adjacency based on natural cubic spline regression
AFcorMI | Prediction of Weighted Mutual Information Adjacency Matrix by Correlation
alignExpr | Align expression data with given vector
allocateJobs | Divide tasks among workers
allowWGCNAThreads | Allow and disable multi-threading for certain WGCNA calculations
automaticNetworkScreening | One-step automatic network gene screening
automaticNetworkScreeningGS | One-step automatic network gene screening with external gene significance

## -- B --

BD.actualFileNames | Various basic operations on 'BlockwiseData' objects.
BD.blockLengths | Various basic operations on 'BlockwiseData' objects.
BD.checkAndDeleteFiles | Various basic operations on 'BlockwiseData' objects.
BD.getData | Various basic operations on 'BlockwiseData' objects.
BD.getMetaData | Various basic operations on 'BlockwiseData' objects.
BD.nBlocks | Various basic operations on 'BlockwiseData' objects.
bicor | Biweight Midcorrelation
bicorAndPvalue | Calculation of biweight midcorrelations and associated p-values
bicovWeightFactors | Weights used in biweight midcovariance
bicovWeights | Weights used in biweight midcovariance
bicovWeightsFromFactors | Weights used in biweight midcovariance
binarizeCategoricalColumns | Turn categorical columns into sets of binary indicators
binarizeCategoricalColumns.forPlots | Turn categorical columns into sets of binary indicators
binarizeCategoricalColumns.forRegression | Turn categorical columns into sets of binary indicators
binarizeCategoricalColumns.pairwise | Turn categorical columns into sets of binary indicators
binarizeCategoricalVariable | Turn a categorical variable into a set of binary indicators
BlockInformation | Create a list holding information about dividing data into blocks
blockSize | Attempt to calculate an appropriate block size to maximize efficiency of block-wise calcualtions.
blockwiseConsensusModules | Find consensus modules across several datasets.
BlockwiseData | Create, merge and expand BlockwiseData objects
blockwiseIndividualTOMs | Calculation of block-wise topological overlaps
blockwiseModules | Automatic network construction and module detection
BloodLists | Blood Cell Types with Corresponding Gene Markers
blueWhiteRed | Blue-white-red color sequence
BrainLists | Brain-Related Categories with Corresponding Gene Markers
BrainRegionMarkers | Gene Markers for Regions of the Human Brain
branchEigengeneDissim | Branch dissimilarity based on eigennodes (eigengenes).
branchEigengeneSimilarity | Branch dissimilarity based on eigennodes (eigengenes).
branchSplit | Branch split.
branchSplit.dissim | Branch split based on dissimilarity.
branchSplitFromStabilityLabels | Branch split (dissimilarity) statistics derived from labels determined from a stability study
branchSplitFromStabilityLabels.individualFraction | Branch split (dissimilarity) statistics derived from labels determined from a stability study
branchSplitFromStabilityLabels.prediction | Branch split (dissimilarity) statistics derived from labels determined from a stability study

## -- C --

checkAdjMat | Check adjacency matrix
checkSets | Check structure and retrieve sizes of a group of datasets.
checkSimilarity | Check adjacency matrix
chooseOneHubInEachModule | Chooses a single hub gene in each module
chooseTopHubInEachModule | Chooses the top hub gene in each module
clusterCoef | Clustering coefficient calculation
coClustering | Co-clustering measure of cluster preservation between two clusterings
coClustering.permutationTest | Permutation test for co-clustering
collapseRows | Select one representative row per group
collapseRowsUsingKME | Selects one representative row per group based on kME
collectGarbage | Iterative garbage collection.
colQuantileC | Fast colunm- and row-wise quantile of a matrix.
conformityBasedNetworkConcepts | Calculation of conformity-based network concepts.
conformityDecomposition | Conformity and module based decomposition of a network adjacency matrix.
consensusCalculation | Calculation of a (single) consenus with optional data calibration.
consensusDissTOMandTree | Consensus clustering based on topological overlap and hierarchical clustering
consensusKME | Calculate consensus kME (eigengene-based connectivities) across multiple data sets.
consensusMEDissimilarity | Consensus dissimilarity of module eigengenes.
ConsensusOptions | Create a list holding consensus calculation options.
consensusOrderMEs | Put close eigenvectors next to each other in several sets.
consensusProjectiveKMeans | Consensus projective K-means (pre-)clustering of expression data
consensusRepresentatives | Consensus selection of group representatives
consensusTOM | Consensus network (topological overlap).
ConsensusTree | Create a new consensus tree
consensusTreeInputs | Get all elementary inputs in a consensus tree
convertNumericColumnsToNumeric | Convert character columns that represent numbers to numeric
cor | Fast calculations of Pearson correlation.
cor1 | Fast calculations of Pearson correlation.
corAndPvalue | Calculation of correlations and associated p-values
corFast | Fast calculations of Pearson correlation.
corPredictionSuccess | Qunatification of success of gene screening
corPvalueFisher | Fisher's asymptotic p-value for correlation
corPvalueStudent | Student asymptotic p-value for correlation
CorrelationOptions | Creates a list of correlation options.
correlationPreservation | Preservation of eigengene correlations
coxRegressionResiduals | Deviance- and martingale residuals from a Cox regression model
cutreeStatic | Constant-height tree cut
cutreeStaticColor | Constant height tree cut using color labels

## -- D --

disableWGCNAThreads | Allow and disable multi-threading for certain WGCNA calculations
displayColors | Show colors used to label modules
dynamicMergeCut | Threshold for module merging

## -- E --

empiricalBayesLM | Empirical Bayes-moderated adjustment for unwanted covariates
enableWGCNAThreads | Allow and disable multi-threading for certain WGCNA calculations
exportNetworkToCytoscape | Export network to Cytoscape
exportNetworkToVisANT | Export network data in format readable by VisANT

## -- F --

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.

## -- G --

GOenrichmentAnalysis | Calculation of GO enrichment (experimental)
goodGenes | Filter genes with too many missing entries
goodGenesMS | Filter genes with too many missing entries across multiple sets
goodSamples | Filter samples with too many missing entries
goodSamplesGenes | Iterative filtering of samples and genes with too many missing entries
goodSamplesGenesMS | Iterative filtering of samples and genes with too many missing entries across multiple data sets
goodSamplesMS | Filter samples with too many missing entries across multiple data sets
greenBlackRed | Green-black-red color sequence
greenWhiteRed | Green-white-red color sequence
GTOMdist | Generalized Topological Overlap Measure

## -- H --

hierarchicalBranchEigengeneDissim | Branch dissimilarity based on eigennodes (eigengenes).
hierarchicalConsensusCalculation | Hierarchical consensus calculation
hierarchicalConsensusKME | Calculation of measures of fuzzy module membership (KME) in hierarchical consensus modules
hierarchicalConsensusMEDissimilarity | Hierarchical consensus calculation of module eigengene dissimilarity
hierarchicalConsensusModules | Hierarchical consensus network construction and module identification
hierarchicalConsensusTOM | Calculation of hierarchical consensus topological overlap matrix
hierarchicalMergeCloseModules | Merge close (similar) hierarchical consensus modules
hubGeneSignificance | Hubgene significance

## -- I --

ImmunePathwayLists | Immune Pathways with Corresponding Gene Markers
imputeByModule | Impute missing data separately in each module
individualTOMs | Calculate individual correlation network matrices
initProgInd | Inline display of progress
intramodularConnectivity | Calculation of intramodular connectivity
intramodularConnectivity.fromExpr | Calculation of intramodular connectivity
isMultiData | Determine whether the supplied object is a valid multiData structure

## -- K --

keepCommonProbes | Keep probes that are shared among given data sets
kMEcomparisonScatterplot | Function to plot kME values between two comparable data sets.

## -- L --

labeledBarplot | Barplot with text or color labels.
labeledHeatmap | Produce a labeled heatmap plot
labeledHeatmap.multiPage | Labeled heatmap divided into several separate plots.
labelPoints | Label scatterplot points
labels2colors | Convert numerical labels to colors.
list2multiData | Convert a list to a multiData structure and vice-versa.
lowerTri2matrix | Reconstruct a symmetric matrix from a distance (lower-triangular) representation

## -- M --

matchLabels | Relabel module labels to best match the given reference labels
matrixToNetwork | Construct a network from a matrix
mergeBlockwiseData | Create, merge and expand BlockwiseData objects
mergeCloseModules | Merge close modules in gene expression data
metaAnalysis | Meta-analysis of binary and continuous variables
metaZfunction | Meta-analysis Z statistic
minWhichMin | Fast joint calculation of row- or column-wise minima and indices of minimum elements
moduleColor.getMEprefix | Get the prefix used to label module eigengenes.
moduleEigengenes | Calculate module eigengenes.
moduleMergeUsingKME | Merge modules and reassign genes using kME.
moduleNumber | Fixed-height cut of a dendrogram.
modulePreservation | Calculation of module preservation statistics
mtd.apply | Apply a function to each set in a multiData structure.
mtd.applyToSubset | Apply a function to each set in a multiData structure.
mtd.branchEigengeneDissim | Branch dissimilarity based on eigennodes (eigengenes).
mtd.colnames | Get and set column names in a multiData structure.
mtd.mapply | Apply a function to elements of given multiData structures.
mtd.rbindSelf | Turn a multiData structure into a single matrix or data frame.
mtd.setAttr | Set attributes on each component of a multiData structure
mtd.setColnames | Get and set column names in a multiData structure.
mtd.simplify | If possible, simplify a multiData structure to a 3-dimensional array.
mtd.subset | Subset rows and columns in a multiData structure
multiData | Create a multiData structure.
multiData.eigengeneSignificance | Eigengene significance across multiple sets
multiData2list | Convert a list to a multiData structure and vice-versa.
multiGrep | Analogs of grep(l) and (g)sub for multiple patterns and relacements
multiGrepl | Analogs of grep(l) and (g)sub for multiple patterns and relacements
multiGSub | Analogs of grep(l) and (g)sub for multiple patterns and relacements
multiIntersect | Union and intersection of multiple sets
multiSetMEs | Calculate module eigengenes.
multiSub | Analogs of grep(l) and (g)sub for multiple patterns and relacements
multiUnion | Union and intersection of multiple sets
mutualInfoAdjacency | Calculate weighted adjacency matrices based on mutual information

## -- N --

nearestCentroidPredictor | Nearest centroid predictor
nearestNeighborConnectivity | Connectivity to a constant number of nearest neighbors
nearestNeighborConnectivityMS | Connectivity to a constant number of nearest neighbors across multiple data sets
networkConcepts | Calculations of network concepts
NetworkOptions | Create a list of network construction arguments (options).
networkScreening | Identification of genes related to a trait
networkScreeningGS | Network gene screening with an external gene significance measure
newBlockInformation | Create a list holding information about dividing data into blocks
newBlockwiseData | Create, merge and expand BlockwiseData objects
newConsensusOptions | Create a list holding consensus calculation options.
newConsensusTree | Create a new consensus tree
newCorrelationOptions | Creates a list of correlation options.
newNetworkOptions | Create a list of network construction arguments (options).
normalizeLabels | Transform numerical labels into normal order.
nPresent | Number of present data entries.
nSets | Number of sets in a multi-set variable
numbers2colors | Color representation for a numeric variable

## -- O --

orderBranchesUsingHubGenes | Optimize dendrogram using branch swaps and reflections.
orderMEs | Put close eigenvectors next to each other
orderMEsByHierarchicalConsensus | Order module eigengenes by their hierarchical consensus similarity
overlapTable | Calculate overlap of modules
overlapTableUsingKME | Determines significant overlap between modules in two networks based on kME tables.

## -- P --

pickHardThreshold | Analysis of scale free topology for hard-thresholding.
pickHardThreshold.fromSimilarity | Analysis of scale free topology for hard-thresholding.
pickSoftThreshold | Analysis of scale free topology for soft-thresholding
pickSoftThreshold.fromSimilarity | Analysis of scale free topology for soft-thresholding
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
plotEigengeneNetworks | Eigengene network plot
plotMat | Red and Green Color Image of Data Matrix
plotMEpairs | Pairwise scatterplots of eigengenes
plotModuleSignificance | Barplot of module significance
plotMultiHist | Plot multiple histograms in a single plot
plotNetworkHeatmap | Network heatmap plot
plotOrderedColors | Plot color rows in a given order, for example under a dendrogram
pmean | Parallel quantile, median, mean
pmean.fromList | Parallel quantile, median, mean
pmedian | Parallel quantile, median, mean
pminWhich.fromList | Parallel quantile, median, mean
populationMeansInAdmixture | Estimate the population-specific mean values in an admixed population.
pquantile | Parallel quantile, median, mean
pquantile.fromList | Parallel quantile, median, mean
prepComma | Prepend a comma to a non-empty string
prependZeros | Pad numbers with leading zeros to specified total width
prependZeros.int | Pad numbers with leading zeros to specified total width
preservationNetworkConnectivity | Network preservation calculations
projectiveKMeans | Projective K-means (pre-)clustering of expression data
proportionsInAdmixture | Estimate the proportion of pure populations in an admixed population based on marker expression values.
propVarExplained | Proportion of variance explained by eigengenes.
pruneAndMergeConsensusModules | Iterative pruning and merging of (hierarchical) consensus modules
pruneConsensusModules | Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity
PWLists | Pathways with Corresponding Gene Markers - Compiled by Mike Palazzolo and Jim Wang from CHDI

## -- Q --

qvalue | Estimate the q-values for a given set of p-values
qvalue.restricted | qvalue convenience wrapper

## -- R --

randIndex | Rand index of two partitions
rankPvalue | Estimate the p-value for ranking consistently high (or low) on multiple lists
recutBlockwiseTrees | Repeat blockwise module detection from pre-calculated data
recutConsensusTrees | Repeat blockwise consensus module detection from pre-calculated data
redWhiteGreen | Red-white-green color sequence
reflectBranch | Select, swap, or reflect branches in a dendrogram.
relativeCorPredictionSuccess | Compare prediction success
removeGreyME | Removes the grey eigengene from a given collection of eigengenes.
removePrincipalComponents | Remove leading principal components from data
replaceMissing | Replace missing values with a constant.
returnGeneSetsAsList | Return pre-defined gene lists in several biomedical categories.
rgcolors.func | Red and Green Color Specification
rowQuantileC | Fast colunm- and row-wise quantile of a matrix.

## -- S --

sampledBlockwiseModules | Blockwise module identification in sampled data
sampledHierarchicalConsensusModules | Hierarchical consensus module identification in sampled data
scaleFreeFitIndex | Calculation of fitting statistics for evaluating scale free topology fit.
scaleFreePlot | Visual check of scale-free topology
SCsLists | Stem Cell-Related Genes with Corresponding Gene Markers
selectBranch | Select, swap, or reflect branches in a dendrogram.
selectFewestConsensusMissing | Select columns with the lowest consensus number of missing data
setCorrelationPreservation | Summary correlation preservation measure
shortenStrings | Shorten given character strings by truncating at a suitable separator.
sigmoidAdjacencyFunction | Sigmoid-type adacency function.
signedKME | Signed eigengene-based connectivity
signifNumeric | Round numeric columns to given significant digits.
signumAdjacencyFunction | Hard-thresholding adjacency function
simpleConsensusCalculation | Simple calculation of a single consenus
simpleHierarchicalConsensusCalculation | Simple hierarchical consensus calculation
simulateDatExpr | Simulation of expression data
simulateDatExpr5Modules | Simplified simulation of expression data
simulateEigengeneNetwork | Simulate eigengene network from a causal model
simulateModule | Simulate a gene co-expression module
simulateMultiExpr | Simulate multi-set expression data
simulateSmallLayer | Simulate small modules
sizeGrWindow | Opens a graphics window with specified dimensions
sizeRestrictedClusterMerge | Cluter merging with size restrictions
softConnectivity | Calculates connectivity of a weighted network.
softConnectivity.fromSimilarity | Calculates connectivity of a weighted network.
spaste | Space-less paste
standardColors | Colors this library uses for labeling modules.
standardScreeningBinaryTrait | Standard screening for binatry traits
standardScreeningCensoredTime | Standard Screening with regard to a Censored Time Variable
standardScreeningNumericTrait | Standard screening for numeric traits
stdErr | Standard error of the mean of a given vector.
stratifiedBarplot | Bar plots of data across two splitting parameters
subsetTOM | Topological overlap for a subset of a whole set of genes
swapTwoBranches | Select, swap, or reflect branches in a dendrogram.

## -- T --

TOMdist | Topological overlap matrix similarity and dissimilarity
TOMplot | Graphical representation of the Topological Overlap Matrix
TOMsimilarity | Topological overlap matrix similarity and dissimilarity
TOMsimilarityFromExpr | Topological overlap matrix
transposeBigData | Transpose a big matrix or data frame
TrueTrait | Estimate the true trait underlying a list of surrogate markers.

## -- U --

unsignedAdjacency | Calculation of unsigned adjacency
updateProgInd | Inline display of progress
userListEnrichment | Measure enrichment between inputted and user-defined lists

## -- V --

vectorizeMatrix | Turn a matrix into a vector of non-redundant components
vectorTOM | Topological overlap for a subset of the whole set of genes
verboseBarplot | Barplot with error bars, annotated by Kruskal-Wallis or ANOVA p-value
verboseBoxplot | Boxplot annotated by a Kruskal-Wallis p-value
verboseIplot | Scatterplot with density
verboseScatterplot | Scatterplot annotated by regression line and p-value
votingLinearPredictor | Voting linear predictor

## -- W --

WGCNAnThreads | Allow and disable multi-threading for certain WGCNA calculations