| Accuracy measures for a 2x2 confusion matrix or for vectors of predicted and observed values. | accuracyMeasures |
| Add error bars to a barplot. | addErrorBars |
| Add grid lines to an existing plot. | addGrid |
| Add vertical ``guide lines'' to a dendrogram plot | addGuideLines |
| Add trait information to multi-set module eigengene structure | addTraitToMEs |
| Calculate network adjacency | adjacency adjacency.fromSimilarity |
| Adjacency matrix based on polynomial regression | adjacency.polyReg |
| Calculate network adjacency based on natural cubic spline regression | adjacency.splineReg |
| Prediction of Weighted Mutual Information Adjacency Matrix by Correlation | AFcorMI |
| Align expression data with given vector | alignExpr |
| Divide tasks among workers | allocateJobs |
| Allow and disable multi-threading for certain WGCNA calculations | allowWGCNAThreads disableWGCNAThreads enableWGCNAThreads WGCNAnThreads |
| One-step automatic network gene screening | automaticNetworkScreening |
| One-step automatic network gene screening with external gene significance | automaticNetworkScreeningGS |
| Various basic operations on 'BlockwiseData' objects. | BD.actualFileNames BD.blockLengths BD.checkAndDeleteFiles BD.getData BD.getMetaData BD.nBlocks |
| Biweight Midcorrelation | bicor |
| Calculation of biweight midcorrelations and associated p-values | bicorAndPvalue |
| Weights used in biweight midcovariance | bicovWeightFactors bicovWeights bicovWeightsFromFactors |
| Turn categorical columns into sets of binary indicators | binarizeCategoricalColumns binarizeCategoricalColumns.forPlots binarizeCategoricalColumns.forRegression binarizeCategoricalColumns.pairwise |
| Turn a categorical variable into a set of binary indicators | binarizeCategoricalVariable |
| Attempt to calculate an appropriate block size to maximize efficiency of block-wise calcualtions. | blockSize |
| Find consensus modules across several datasets. | blockwiseConsensusModules |
| Calculation of block-wise topological overlaps | blockwiseIndividualTOMs |
| Automatic network construction and module detection | blockwiseModules |
| Blood Cell Types with Corresponding Gene Markers | BloodLists |
| Blue-white-red color sequence | blueWhiteRed |
| Brain-Related Categories with Corresponding Gene Markers | BrainLists |
| Gene Markers for Regions of the Human Brain | BrainRegionMarkers |
| Branch dissimilarity based on eigennodes (eigengenes). | branchEigengeneDissim branchEigengeneSimilarity hierarchicalBranchEigengeneDissim mtd.branchEigengeneDissim |
| Branch split. | branchSplit |
| Branch split based on dissimilarity. | branchSplit.dissim |
| Branch split (dissimilarity) statistics derived from labels determined from a stability study | branchSplitFromStabilityLabels branchSplitFromStabilityLabels.individualFraction branchSplitFromStabilityLabels.prediction |
| Check adjacency matrix | checkAdjMat checkSimilarity |
| Check structure and retrieve sizes of a group of datasets. | checkSets |
| Chooses a single hub gene in each module | chooseOneHubInEachModule |
| Chooses the top hub gene in each module | chooseTopHubInEachModule |
| Clustering coefficient calculation | clusterCoef |
| Co-clustering measure of cluster preservation between two clusterings | coClustering |
| Permutation test for co-clustering | coClustering.permutationTest |
| Select one representative row per group | collapseRows |
| Selects one representative row per group based on kME | collapseRowsUsingKME |
| Iterative garbage collection. | collectGarbage |
| Fast colunm- and row-wise quantile of a matrix. | colQuantileC rowQuantileC |
| Calculation of conformity-based network concepts. | conformityBasedNetworkConcepts |
| Conformity and module based decomposition of a network adjacency matrix. | conformityDecomposition |
| Calculation of a (single) consenus with optional data calibration. | consensusCalculation |
| Consensus clustering based on topological overlap and hierarchical clustering | consensusDissTOMandTree |
| Calculate consensus kME (eigengene-based connectivities) across multiple data sets. | consensusKME |
| Consensus dissimilarity of module eigengenes. | consensusMEDissimilarity |
| Put close eigenvectors next to each other in several sets. | consensusOrderMEs |
| Consensus projective K-means (pre-)clustering of expression data | consensusProjectiveKMeans |
| Consensus selection of group representatives | consensusRepresentatives |
| Consensus network (topological overlap). | consensusTOM |
| Get all elementary inputs in a consensus tree | consensusTreeInputs |
| Convert character columns that represent numbers to numeric | convertNumericColumnsToNumeric |
| Fast calculations of Pearson correlation. | cor cor1 corFast |
| Calculation of correlations and associated p-values | corAndPvalue |
| Qunatification of success of gene screening | corPredictionSuccess |
| Fisher's asymptotic p-value for correlation | corPvalueFisher |
| Student asymptotic p-value for correlation | corPvalueStudent |
| Preservation of eigengene correlations | correlationPreservation |
| Deviance- and martingale residuals from a Cox regression model | coxRegressionResiduals |
| Constant-height tree cut | cutreeStatic |
| Constant height tree cut using color labels | cutreeStaticColor |
| Show colors used to label modules | displayColors |
| Threshold for module merging | dynamicMergeCut |
| Empirical Bayes-moderated adjustment for unwanted covariates | empiricalBayesLM |
| Export network to Cytoscape | exportNetworkToCytoscape |
| Export network data in format readable by VisANT | exportNetworkToVisANT |
| Turn non-numeric columns into factors | factorizeNonNumericColumns |
| Put single-set data into a form useful for multiset calculations. | fixDataStructure |
| Break long character strings into multiple lines | formatLabels |
| Calculation of fundamental network concepts from an adjacency matrix. | fundamentalNetworkConcepts |
| Calculation of GO enrichment (experimental) | GOenrichmentAnalysis |
| Filter genes with too many missing entries | goodGenes |
| Filter genes with too many missing entries across multiple sets | goodGenesMS |
| Filter samples with too many missing entries | goodSamples |
| Iterative filtering of samples and genes with too many missing entries | goodSamplesGenes |
| Iterative filtering of samples and genes with too many missing entries across multiple data sets | goodSamplesGenesMS |
| Filter samples with too many missing entries across multiple data sets | goodSamplesMS |
| Green-black-red color sequence | greenBlackRed |
| Green-white-red color sequence | greenWhiteRed |
| Generalized Topological Overlap Measure | GTOMdist |
| Hierarchical consensus calculation | hierarchicalConsensusCalculation |
| Calculation of measures of fuzzy module membership (KME) in hierarchical consensus modules | hierarchicalConsensusKME |
| Hierarchical consensus calculation of module eigengene dissimilarity | hierarchicalConsensusMEDissimilarity |
| Hierarchical consensus network construction and module identification | hierarchicalConsensusModules |
| Calculation of hierarchical consensus topological overlap matrix | hierarchicalConsensusTOM |
| Merge close (similar) hierarchical consensus modules | hierarchicalMergeCloseModules |
| Hubgene significance | hubGeneSignificance |
| Immune Pathways with Corresponding Gene Markers | ImmunePathwayLists |
| Impute missing data separately in each module | imputeByModule |
| Calculate individual correlation network matrices | individualTOMs |
| Inline display of progress | initProgInd updateProgInd |
| Calculation of intramodular connectivity | intramodularConnectivity intramodularConnectivity.fromExpr |
| Determine whether the supplied object is a valid multiData structure | isMultiData |
| Keep probes that are shared among given data sets | keepCommonProbes |
| Function to plot kME values between two comparable data sets. | kMEcomparisonScatterplot |
| Barplot with text or color labels. | labeledBarplot |
| Produce a labeled heatmap plot | labeledHeatmap |
| Labeled heatmap divided into several separate plots. | labeledHeatmap.multiPage |
| Label scatterplot points | labelPoints |
| Convert numerical labels to colors. | labels2colors |
| Convert a list to a multiData structure and vice-versa. | list2multiData multiData2list |
| Reconstruct a symmetric matrix from a distance (lower-triangular) representation | lowerTri2matrix |
| Relabel module labels to best match the given reference labels | matchLabels |
| Construct a network from a matrix | matrixToNetwork |
| Merge close modules in gene expression data | mergeCloseModules |
| Meta-analysis of binary and continuous variables | metaAnalysis |
| Meta-analysis Z statistic | metaZfunction |
| Fast joint calculation of row- or column-wise minima and indices of minimum elements | minWhichMin |
| Get the prefix used to label module eigengenes. | moduleColor.getMEprefix |
| Calculate module eigengenes. | moduleEigengenes |
| Merge modules and reassign genes using kME. | moduleMergeUsingKME |
| Fixed-height cut of a dendrogram. | moduleNumber |
| Calculation of module preservation statistics | modulePreservation |
| Apply a function to each set in a multiData structure. | mtd.apply mtd.applyToSubset |
| Apply a function to elements of given multiData structures. | mtd.mapply |
| Turn a multiData structure into a single matrix or data frame. | mtd.rbindSelf |
| Set attributes on each component of a multiData structure | mtd.setAttr |
| Get and set column names in a multiData structure. | mtd.colnames mtd.setColnames |
| If possible, simplify a multiData structure to a 3-dimensional array. | mtd.simplify |
| Subset rows and columns in a multiData structure | mtd.subset |
| Create a multiData structure. | multiData |
| Eigengene significance across multiple sets | multiData.eigengeneSignificance |
| Analogs of grep(l) and (g)sub for multiple patterns and relacements | multiGrep multiGrepl multiGSub multiSub |
| Calculate module eigengenes. | multiSetMEs |
| Union and intersection of multiple sets | multiIntersect multiUnion |
| Calculate weighted adjacency matrices based on mutual information | mutualInfoAdjacency |
| Nearest centroid predictor | nearestCentroidPredictor |
| Connectivity to a constant number of nearest neighbors | nearestNeighborConnectivity |
| Connectivity to a constant number of nearest neighbors across multiple data sets | nearestNeighborConnectivityMS |
| Calculations of network concepts | networkConcepts |
| Identification of genes related to a trait | networkScreening |
| Network gene screening with an external gene significance measure | networkScreeningGS |
| Create a list holding information about dividing data into blocks | BlockInformation newBlockInformation |
| Create, merge and expand BlockwiseData objects | addBlockToBlockwiseData BlockwiseData mergeBlockwiseData newBlockwiseData |
| Create a list holding consensus calculation options. | ConsensusOptions newConsensusOptions |
| Create a new consensus tree | ConsensusTree newConsensusTree |
| Creates a list of correlation options. | CorrelationOptions newCorrelationOptions |
| Create a list of network construction arguments (options). | NetworkOptions newNetworkOptions |
| Transform numerical labels into normal order. | normalizeLabels |
| Number of present data entries. | nPresent |
| Number of sets in a multi-set variable | nSets |
| Color representation for a numeric variable | numbers2colors |
| Optimize dendrogram using branch swaps and reflections. | orderBranchesUsingHubGenes |
| Put close eigenvectors next to each other | orderMEs |
| Order module eigengenes by their hierarchical consensus similarity | orderMEsByHierarchicalConsensus |
| Calculate overlap of modules | overlapTable |
| Determines significant overlap between modules in two networks based on kME tables. | overlapTableUsingKME |
| Analysis of scale free topology for hard-thresholding. | pickHardThreshold pickHardThreshold.fromSimilarity |
| Analysis of scale free topology for soft-thresholding | pickSoftThreshold pickSoftThreshold.fromSimilarity |
| Annotated clustering dendrogram of microarray samples | plotClusterTreeSamples |
| Plot color rows in a given order, for example under a dendrogram | plotColorUnderTree plotOrderedColors |
| Red and Green Color Image of Correlation Matrix | plotCor |
| Dendrogram plot with color annotation of objects | plotDendroAndColors |
| Eigengene network plot | plotEigengeneNetworks |
| Red and Green Color Image of Data Matrix | plotMat |
| Pairwise scatterplots of eigengenes | plotMEpairs |
| Barplot of module significance | plotModuleSignificance |
| Plot multiple histograms in a single plot | plotMultiHist |
| Network heatmap plot | plotNetworkHeatmap |
| Estimate the population-specific mean values in an admixed population. | populationMeansInAdmixture |
| Parallel quantile, median, mean | pmean pmean.fromList pmedian pminWhich.fromList pquantile pquantile.fromList |
| Prepend a comma to a non-empty string | prepComma |
| Pad numbers with leading zeros to specified total width | prependZeros prependZeros.int |
| Network preservation calculations | preservationNetworkConnectivity |
| Projective K-means (pre-)clustering of expression data | projectiveKMeans |
| Estimate the proportion of pure populations in an admixed population based on marker expression values. | proportionsInAdmixture |
| Proportion of variance explained by eigengenes. | propVarExplained |
| Iterative pruning and merging of (hierarchical) consensus modules | pruneAndMergeConsensusModules |
| Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity | pruneConsensusModules |
| Pathways with Corresponding Gene Markers - Compiled by Mike Palazzolo and Jim Wang from CHDI | PWLists |
| Estimate the q-values for a given set of p-values | qvalue |
| qvalue convenience wrapper | qvalue.restricted |
| Rand index of two partitions | randIndex |
| Estimate the p-value for ranking consistently high (or low) on multiple lists | rankPvalue |
| Repeat blockwise module detection from pre-calculated data | recutBlockwiseTrees |
| Repeat blockwise consensus module detection from pre-calculated data | recutConsensusTrees |
| Red-white-green color sequence | redWhiteGreen |
| Compare prediction success | relativeCorPredictionSuccess |
| Removes the grey eigengene from a given collection of eigengenes. | removeGreyME |
| Remove leading principal components from data | removePrincipalComponents |
| Replace missing values with a constant. | replaceMissing |
| Return pre-defined gene lists in several biomedical categories. | returnGeneSetsAsList |
| Red and Green Color Specification | rgcolors.func |
| Blockwise module identification in sampled data | sampledBlockwiseModules |
| Hierarchical consensus module identification in sampled data | sampledHierarchicalConsensusModules |
| Calculation of fitting statistics for evaluating scale free topology fit. | scaleFreeFitIndex |
| Visual check of scale-free topology | scaleFreePlot |
| Stem Cell-Related Genes with Corresponding Gene Markers | SCsLists |
| Select columns with the lowest consensus number of missing data | selectFewestConsensusMissing |
| Summary correlation preservation measure | setCorrelationPreservation |
| Shorten given character strings by truncating at a suitable separator. | shortenStrings |
| Sigmoid-type adacency function. | sigmoidAdjacencyFunction |
| Signed eigengene-based connectivity | signedKME |
| Round numeric columns to given significant digits. | signifNumeric |
| Hard-thresholding adjacency function | signumAdjacencyFunction |
| Simple calculation of a single consenus | simpleConsensusCalculation |
| Simple hierarchical consensus calculation | simpleHierarchicalConsensusCalculation |
| Simulation of expression data | simulateDatExpr |
| Simplified simulation of expression data | simulateDatExpr5Modules |
| Simulate eigengene network from a causal model | simulateEigengeneNetwork |
| Simulate a gene co-expression module | simulateModule |
| Simulate multi-set expression data | simulateMultiExpr |
| Simulate small modules | simulateSmallLayer |
| Opens a graphics window with specified dimensions | sizeGrWindow |
| Cluter merging with size restrictions | sizeRestrictedClusterMerge |
| Calculates connectivity of a weighted network. | softConnectivity softConnectivity.fromSimilarity |
| Space-less paste | spaste |
| Colors this library uses for labeling modules. | standardColors |
| Standard screening for binatry traits | standardScreeningBinaryTrait |
| Standard Screening with regard to a Censored Time Variable | standardScreeningCensoredTime |
| Standard screening for numeric traits | standardScreeningNumericTrait |
| Standard error of the mean of a given vector. | stdErr |
| Bar plots of data across two splitting parameters | stratifiedBarplot |
| Topological overlap for a subset of a whole set of genes | subsetTOM |
| Select, swap, or reflect branches in a dendrogram. | reflectBranch selectBranch swapTwoBranches |
| Graphical representation of the Topological Overlap Matrix | TOMplot |
| Topological overlap matrix similarity and dissimilarity | TOMdist TOMsimilarity |
| Topological overlap matrix | TOMsimilarityFromExpr |
| Transpose a big matrix or data frame | transposeBigData |
| Estimate the true trait underlying a list of surrogate markers. | TrueTrait |
| Calculation of unsigned adjacency | unsignedAdjacency |
| Measure enrichment between inputted and user-defined lists | userListEnrichment |
| Turn a matrix into a vector of non-redundant components | vectorizeMatrix |
| Topological overlap for a subset of the whole set of genes | vectorTOM |
| Barplot with error bars, annotated by Kruskal-Wallis or ANOVA p-value | verboseBarplot |
| Boxplot annotated by a Kruskal-Wallis p-value | verboseBoxplot |
| Scatterplot with density | verboseIplot |
| Scatterplot annotated by regression line and p-value | verboseScatterplot |
| Voting linear predictor | votingLinearPredictor |
