{ "_id": "6a1d50081d7bb097a0a459e9", "Package": "WGCNA", "Version": "1.74", "Date": "2026-01-27", "Title": "Weighted Correlation Network Analysis", "Maintainer": "Peter Langfelder ", "ZipData": "no", "License": "GPL (>= 2)", "Authors@R": "c(person(given = \"Peter\",\nfamily = \"Langfelder\",\nrole = c(\"aut\", \"cre\"),\nemail = \"Peter.Langfelder@gmail.com\"),\nperson(given = \"Steve\",\nfamily = \"Horvath\",\nrole = \"aut\"),\nperson(given = \"Chaochao\",\nfamily = \"Cai\",\nrole = \"aut\"),\nperson(given = \"Jun\",\nfamily = \"Dong\",\nrole = \"aut\"),\nperson(given = \"Jeremy\",\nfamily = \"Miller\",\nrole = \"aut\"),\nperson(given = \"Lin\",\nfamily = \"Song\",\nrole = \"aut\"),\nperson(given = \"Andy\",\nfamily = \"Yip\",\nrole = \"aut\"),\nperson(given = \"Bin\",\nfamily = \"Zhang\",\nrole = \"aut\"))", "Description": "Functions necessary to perform Weighted Correlation\nNetwork Analysis on high-dimensional data as originally\ndescribed in Horvath and Zhang (2005)\n and Langfelder and Horvath (2008)\n. Includes functions for\nrudimentary data cleaning, construction of correlation\nnetworks, module identification, summarization, and relating of\nvariables and modules to sample traits. Also includes a number\nof utility functions for data manipulation and visualization.", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-01 09:15:33 UTC", "User": "root" }, "Author": "Peter Langfelder [aut, cre], Steve Horvath [aut], Chaochao Cai\n[aut], Jun Dong [aut], Jeremy Miller [aut], Lin Song [aut],\nAndy Yip [aut], Bin Zhang [aut]", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://plangfelder.r-universe.dev", "Date/Publication": "2026-01-30 06:10:44 UTC", "RemoteUrl": "https://github.com/cran/WGCNA", "RemoteRef": "HEAD", "RemoteSha": "61a2a7d38d88316d34bc75249cf1849cae33ab2e", "MD5sum": "d8c3f9bc11a556521efe9a4c77804220", "_user": "plangfelder", "_type": "src", "_file": "WGCNA_1.74.tar.gz", "_fileid": "589d320afdecef9764e5d0904a866cc2e79652c282488229d429d825ed5a1c18", "_filesize": 2498083, "_sha256": "589d320afdecef9764e5d0904a866cc2e79652c282488229d429d825ed5a1c18", "_created": "2026-06-01T09:15:33.000Z", "_published": 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block size to maximize efficiency of block-wise calcualtions.", "topics": [ "blockSize" ] }, { "page": "blockwiseConsensusModules", "title": "Find consensus modules across several datasets.", "topics": [ "blockwiseConsensusModules" ] }, { "page": "blockwiseIndividualTOMs", "title": "Calculation of block-wise topological overlaps", "topics": [ "blockwiseIndividualTOMs" ] }, { "page": "blockwiseModules", "title": "Automatic network construction and module detection", "topics": [ "blockwiseModules" ] }, { "page": "BloodLists", "title": "Blood Cell Types with Corresponding Gene Markers", "topics": [ "BloodLists" ] }, { "page": "blueWhiteRed", "title": "Blue-white-red color sequence", "topics": [ "blueWhiteRed" ] }, { "page": "BrainLists", "title": "Brain-Related Categories with Corresponding Gene Markers", "topics": [ "BrainLists" ] }, { "page": "BrainRegionMarkers", "title": "Gene Markers for Regions of the Human Brain", "topics": [ "BrainRegionMarkers" ] }, { "page": 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"title": "Chooses a single hub gene in each module", "topics": [ "chooseOneHubInEachModule" ] }, { "page": "chooseTopHubInEachModule", "title": "Chooses the top hub gene in each module", "topics": [ "chooseTopHubInEachModule" ] }, { "page": "clusterCoef", "title": "Clustering coefficient calculation", "topics": [ "clusterCoef" ] }, { "page": "coClustering", "title": "Co-clustering measure of cluster preservation between two clusterings", "topics": [ "coClustering" ] }, { "page": "coClustering.permutationTest", "title": "Permutation test for co-clustering", "topics": [ "coClustering.permutationTest" ] }, { "page": "collapseRows", "title": "Select one representative row per group", "topics": [ "collapseRows" ] }, { "page": "collapseRowsUsingKME", "title": "Selects one representative row per group based on kME", "topics": [ "collapseRowsUsingKME" ] }, { "page": "collectGarbage", "title": "Iterative garbage collection.", "topics": [ "collectGarbage" ] }, { "page": "colQuantileC", "title": "Fast colunm- and row-wise quantile of a matrix.", "topics": [ "colQuantileC", "rowQuantileC" ] }, { "page": "conformityBasedNetworkConcepts", "title": "Calculation of conformity-based network concepts.", "topics": [ "conformityBasedNetworkConcepts" ] }, { "page": "conformityDecomposition", "title": "Conformity and module based decomposition of a network adjacency matrix.", "topics": [ "conformityDecomposition" ] }, { "page": "consensusCalculation", "title": "Calculation of a (single) consenus with optional data calibration.", "topics": [ "consensusCalculation" ] }, { "page": "consensusDissTOMandTree", "title": "Consensus clustering based on topological overlap and hierarchical clustering", "topics": [ "consensusDissTOMandTree" ] }, { "page": "consensusKME", "title": "Calculate consensus kME (eigengene-based connectivities) across multiple data sets.", "topics": [ "consensusKME" ] }, { "page": "consensusMEDissimilarity", "title": "Consensus dissimilarity of module eigengenes.", "topics": [ "consensusMEDissimilarity" ] }, { "page": "consensusOrderMEs", "title": "Put close eigenvectors next to each other in several sets.", "topics": [ "consensusOrderMEs" ] }, { "page": "consensusProjectiveKMeans", "title": "Consensus projective K-means (pre-)clustering of expression data", "topics": [ "consensusProjectiveKMeans" ] }, { "page": "consensusRepresentatives", "title": "Consensus selection of group representatives", "topics": [ "consensusRepresentatives" ] }, { "page": "consensusTOM", "title": "Consensus network (topological overlap).", "topics": [ "consensusTOM" ] }, { "page": "consensusTreeInputs", "title": "Get all elementary inputs in a consensus tree", "topics": [ "consensusTreeInputs" ] }, { "page": "convertNumericColumnsToNumeric", "title": "Convert character columns that represent numbers to numeric", "topics": [ "convertNumericColumnsToNumeric" ] }, { "page": "cor", "title": "Fast calculations of Pearson correlation.", "topics": [ "cor", "cor1", "corFast" ] }, { "page": "corAndPvalue", "title": "Calculation of correlations and associated p-values", "topics": [ "corAndPvalue" ] }, { "page": "corPredictionSuccess", "title": "Qunatification of success of gene screening", "topics": [ "corPredictionSuccess" ] }, { "page": "corPvalueFisher", "title": "Fisher's asymptotic p-value for correlation", "topics": [ "corPvalueFisher" ] }, { "page": "corPvalueStudent", "title": "Student asymptotic p-value for correlation", "topics": [ "corPvalueStudent" ] }, { "page": "correlationPreservation", "title": "Preservation of eigengene correlations", "topics": [ "correlationPreservation" ] }, { "page": "coxRegressionResiduals", "title": "Deviance- and martingale residuals from a Cox regression model", "topics": [ "coxRegressionResiduals" ] }, { "page": "cutreeStatic", "title": "Constant-height tree cut", "topics": [ "cutreeStatic" ] }, { "page": "cutreeStaticColor", "title": "Constant height tree cut using color labels", "topics": [ "cutreeStaticColor" ] }, { "page": "displayColors", "title": "Show colors used to label modules", "topics": [ "displayColors" ] }, { "page": "dynamicMergeCut", "title": "Threshold for module merging", "topics": [ "dynamicMergeCut" ] }, { "page": "empiricalBayesLM", "title": "Empirical Bayes-moderated adjustment for unwanted covariates", "topics": [ "empiricalBayesLM" ] }, { "page": "exportNetworkToCytoscape", "title": "Export network to Cytoscape", "topics": [ "exportNetworkToCytoscape" ] }, { "page": "exportNetworkToVisANT", "title": "Export network data in format readable by VisANT", "topics": [ "exportNetworkToVisANT" ] }, { "page": "factorizeNonNumericColumns", "title": "Turn non-numeric columns into factors", "topics": [ "factorizeNonNumericColumns" ] }, { "page": "fixDataStructure", "title": "Put single-set data into a form useful for multiset calculations.", "topics": [ "fixDataStructure" ] }, { "page": "formatLabels", "title": "Break long character strings into multiple lines", "topics": [ "formatLabels" ] }, { "page": "fundamentalNetworkConcepts", "title": "Calculation of fundamental network concepts from an adjacency matrix.", "topics": [ "fundamentalNetworkConcepts" ] }, { "page": "GOenrichmentAnalysis", "title": "Calculation of GO enrichment", "topics": [ "GOenrichmentAnalysis" ] }, { "page": "goodGenes", "title": "Filter genes with too many missing entries", "topics": [ "goodGenes" ] }, { "page": "goodGenesMS", "title": "Filter genes with too many missing entries across multiple sets", "topics": [ "goodGenesMS" ] }, { "page": "goodSamples", "title": "Filter samples with too many missing entries", "topics": [ "goodSamples" ] }, { "page": "goodSamplesGenes", "title": "Iterative filtering of samples and genes with too many missing entries", "topics": [ "goodSamplesGenes" ] }, { "page": "goodSamplesGenesMS", "title": "Iterative filtering of samples and genes with too many missing entries across multiple data sets", "topics": [ "goodSamplesGenesMS" ] }, { "page": "goodSamplesMS", "title": "Filter samples with too many missing entries across multiple data sets", "topics": [ "goodSamplesMS" ] }, { "page": "greenBlackRed", "title": "Green-black-red color sequence", "topics": [ "greenBlackRed" ] }, { "page": "greenWhiteRed", "title": "Green-white-red color sequence", "topics": [ "greenWhiteRed" ] }, { "page": "GTOMdist", "title": "Generalized Topological Overlap Measure", "topics": [ "GTOMdist" ] }, { "page": "hierarchicalConsensusCalculation", "title": "Hierarchical consensus calculation", "topics": [ "hierarchicalConsensusCalculation" ] }, { "page": "hierarchicalConsensusKME", "title": "Calculation of measures of fuzzy module membership (KME) in hierarchical consensus modules", "topics": [ "hierarchicalConsensusKME" ] }, { "page": "hierarchicalConsensusMEDissimilarity", "title": "Hierarchical consensus calculation of module eigengene dissimilarity", "topics": [ "hierarchicalConsensusMEDissimilarity" ] }, { "page": "hierarchicalConsensusModules", "title": "Hierarchical consensus network construction and module identification", "topics": [ "hierarchicalConsensusModules" ] }, { "page": "hierarchicalConsensusTOM", "title": "Calculation of hierarchical consensus topological overlap matrix", "topics": [ "hierarchicalConsensusTOM" ] }, { "page": "hierarchicalMergeCloseModules", "title": "Merge close (similar) hierarchical consensus modules", "topics": [ "hierarchicalMergeCloseModules" ] }, { "page": "hubGeneSignificance", "title": "Hubgene significance", "topics": [ "hubGeneSignificance" ] }, { "page": "ImmunePathwayLists", "title": "Immune Pathways with Corresponding Gene Markers", "topics": [ "ImmunePathwayLists" ] }, { "page": "imputeByModule", "title": "Impute missing data separately in each module", "topics": [ "imputeByModule" ] }, { "page": "individualTOMs", "title": "Calculate individual correlation network matrices", "topics": [ "individualTOMs" ] }, { "page": "initProgInd", "title": "Inline display of progress", "topics": [ "initProgInd", "updateProgInd" ] }, { "page": "intramodularConnectivity", "title": "Calculation of intramodular connectivity", "topics": [ "intramodularConnectivity", "intramodularConnectivity.fromExpr" ] }, { "page": "isMultiData", "title": "Determine whether the supplied object is a valid multiData structure", "topics": [ "isMultiData" ] }, { "page": "keepCommonProbes", "title": "Keep probes that are shared among given data sets", "topics": [ "keepCommonProbes" ] }, { "page": "kMEcomparisonScatterplot", "title": "Function to plot kME values between two comparable data sets.", "topics": [ "kMEcomparisonScatterplot" ] }, { "page": "labeledBarplot", "title": "Barplot with text or color labels.", "topics": [ "labeledBarplot" ] }, { "page": "labeledHeatmap", "title": "Produce a labeled heatmap plot", "topics": [ "labeledHeatmap" ] }, { "page": "labeledHeatmap.multiPage", "title": "Labeled heatmap divided into several separate plots.", "topics": [ "labeledHeatmap.multiPage" ] }, { "page": "labelPoints", "title": "Label scatterplot points", "topics": [ "labelPoints" ] }, { "page": "labels2colors", "title": "Convert numerical labels to colors.", "topics": [ "labels2colors" ] }, { "page": "list2multiData", "title": "Convert a list to a multiData structure and vice-versa.", "topics": [ "list2multiData", "multiData2list" ] }, { "page": "lowerTri2matrix", "title": "Reconstruct a symmetric matrix from a distance (lower-triangular) representation", "topics": [ "lowerTri2matrix" ] }, { "page": "matchLabels", "title": "Relabel module labels to best match the given reference labels", "topics": [ "matchLabels" ] }, { "page": "matrixToNetwork", "title": "Construct a network from a matrix", "topics": [ "matrixToNetwork" ] }, { "page": "mergeCloseModules", "title": "Merge close modules in gene expression data", "topics": [ "mergeCloseModules" ] }, { "page": "metaAnalysis", "title": "Meta-analysis of binary and continuous variables", "topics": [ "metaAnalysis" ] }, { "page": "metaZfunction", "title": "Meta-analysis Z statistic", "topics": [ "metaZfunction" ] }, { "page": "minWhichMin", "title": "Fast joint calculation of row- or column-wise minima and indices of minimum elements", "topics": [ "minWhichMin" ] }, { "page": "modifiedBisquareWeights", "title": "Modified Bisquare Weights", "topics": [ "modifiedBisquareWeights" ] }, { "page": "moduleColor.getMEprefix", "title": "Get the prefix used to label module eigengenes.", "topics": [ "moduleColor.getMEprefix" ] }, { "page": "moduleEigengenes", "title": "Calculate module eigengenes.", "topics": [ "moduleEigengenes" ] }, { "page": "moduleMergeUsingKME", "title": "Merge modules and reassign genes using kME.", "topics": [ "moduleMergeUsingKME" ] }, { "page": "moduleNumber", "title": "Fixed-height cut of a dendrogram.", "topics": [ "moduleNumber" ] }, { "page": "modulePreservation", "title": "Calculation of module preservation statistics", "topics": [ "modulePreservation" ] }, { "page": "mtd.apply", "title": "Apply a function to each set in a multiData structure.", "topics": [ "mtd.apply", "mtd.applyToSubset" ] }, { "page": "mtd.mapply", "title": "Apply a function to elements of given multiData structures.", "topics": [ "mtd.mapply" ] }, { "page": "mtd.rbindSelf", "title": "Turn a multiData structure into a single matrix or data frame.", "topics": [ "mtd.rbindSelf" ] }, { "page": "mtd.setAttr", "title": "Set attributes on each component of a multiData structure", "topics": [ "mtd.setAttr" ] }, { "page": "mtd.setColnames", "title": "Get and set column names in a multiData structure.", "topics": [ "mtd.colnames", "mtd.setColnames" ] }, { "page": "mtd.simplify", "title": "If possible, simplify a multiData structure to a 3-dimensional array.", "topics": [ "mtd.simplify" ] }, { "page": "mtd.subset", "title": "Subset rows and columns in a multiData structure", "topics": [ "mtd.subset" ] }, { "page": "multiData", "title": "Create a multiData structure.", "topics": [ "multiData" ] }, { "page": "multiData.eigengeneSignificance", "title": "Eigengene significance across multiple sets", "topics": [ "multiData.eigengeneSignificance" ] }, { "page": "multiGSub", "title": "Analogs of grep(l) and (g)sub for multiple patterns and relacements", "topics": [ "multiGrep", "multiGrepl", "multiGSub", "multiSub" ] }, { "page": "multiSetMEs", "title": "Calculate module eigengenes.", "topics": [ "multiSetMEs" ] }, { "page": "multiUnion", "title": "Union and intersection of multiple sets", "topics": [ "multiIntersect", "multiUnion" ] }, { "page": "mutualInfoAdjacency", "title": "Calculate weighted adjacency matrices based on mutual information", "topics": [ "mutualInfoAdjacency" ] }, { "page": "nearestCentroidPredictor", "title": "Nearest centroid predictor", "topics": [ "nearestCentroidPredictor" ] }, { "page": "nearestNeighborConnectivity", "title": "Connectivity to a constant number of nearest neighbors", "topics": [ "nearestNeighborConnectivity" ] }, { "page": "nearestNeighborConnectivityMS", "title": "Connectivity to a constant number of nearest neighbors across multiple data sets", "topics": [ "nearestNeighborConnectivityMS" ] }, { "page": "networkConcepts", "title": "Calculations of network concepts", "topics": [ "networkConcepts" ] }, { "page": "networkScreening", "title": "Identification of genes related to a trait", "topics": [ "networkScreening" ] }, { "page": "networkScreeningGS", "title": "Network gene screening with an external gene significance measure", "topics": [ "networkScreeningGS" ] }, { "page": "newBlockInformation", "title": "Create a list holding information about dividing data into blocks", "topics": [ "BlockInformation", "newBlockInformation" ] }, { "page": "newBlockwiseData", "title": "Create, merge and expand BlockwiseData objects", "topics": [ "addBlockToBlockwiseData", "BlockwiseData", "mergeBlockwiseData", "newBlockwiseData" ] }, { "page": "newConsensusOptions", "title": "Create a list holding consensus calculation options.", "topics": [ "ConsensusOptions", "newConsensusOptions" ] }, { "page": "newConsensusTree", "title": "Create a new consensus tree", "topics": [ "ConsensusTree", "newConsensusTree" ] }, { "page": "newCorrelationOptions", "title": "Creates a list of correlation options.", "topics": [ "CorrelationOptions", "newCorrelationOptions" ] }, { "page": "newNetworkOptions", "title": "Create a list of network construction arguments (options).", "topics": [ "NetworkOptions", "newNetworkOptions" ] }, { "page": "normalizeLabels", "title": "Transform numerical labels into normal order.", "topics": [ "normalizeLabels" ] }, { "page": "nPresent", "title": "Number of present data entries.", "topics": [ "nPresent" ] }, { "page": "nSets", "title": "Number of sets in a multi-set variable", "topics": [ "nSets" ] }, { "page": "numbers2colors", "title": "Color representation for a numeric variable", "topics": [ "numbers2colors" ] }, { "page": "orderBranchesUsingHubGenes", "title": "Optimize dendrogram using branch swaps and reflections.", "topics": [ "orderBranchesUsingHubGenes" ] }, { "page": "orderMEs", "title": "Put close eigenvectors next to each other", "topics": [ "orderMEs" ] }, { "page": "orderMEsByHierarchicalConsensus", "title": "Order module eigengenes by their hierarchical consensus similarity", "topics": [ "orderMEsByHierarchicalConsensus" ] }, { "page": "overlapTable", "title": "Calculate overlap of modules", "topics": [ "overlapTable" ] }, { "page": "overlapTableUsingKME", "title": "Determines significant overlap between modules in two networks based on kME tables.", "topics": [ "overlapTableUsingKME" ] }, { "page": "pickHardThreshold", "title": "Analysis of scale free topology for hard-thresholding.", "topics": [ "pickHardThreshold", "pickHardThreshold.fromSimilarity" ] }, { "page": "pickSoftThreshold", "title": "Analysis of scale free topology for soft-thresholding", "topics": [ "pickSoftThreshold", "pickSoftThreshold.fromSimilarity" ] }, { "page": "plotClusterTreeSamples", "title": "Annotated clustering dendrogram of microarray samples", "topics": [ "plotClusterTreeSamples" ] }, { "page": "plotColorUnderTree", "title": "Plot color rows in a given order, for example under a dendrogram", "topics": [ "plotColorUnderTree", "plotOrderedColors" ] }, { "page": "plotCor", "title": "Red and Green Color Image of Correlation Matrix", "topics": [ "plotCor" ] }, { "page": "plotDendroAndColors", "title": "Dendrogram plot with color annotation of objects", "topics": [ "plotDendroAndColors" ] }, { "page": "plotEigengeneNetworks", "title": "Eigengene network plot", "topics": [ "plotEigengeneNetworks" ] }, { "page": "plotMat", "title": "Red and Green Color Image of Data Matrix", "topics": [ "plotMat" ] }, { "page": "plotMEpairs", "title": "Pairwise scatterplots of eigengenes", "topics": [ "plotMEpairs" ] }, { "page": "plotModuleSignificance", "title": "Barplot of module significance", "topics": [ "plotModuleSignificance" ] }, { "page": "plotMultiHist", "title": "Plot multiple histograms in a single plot", "topics": [ "plotMultiHist" ] }, { "page": "plotNetworkHeatmap", "title": "Network heatmap plot", "topics": [ "plotNetworkHeatmap" ] }, { "page": "populationMeansInAdmixture", "title": "Estimate the population-specific mean values in an admixed population.", "topics": [ "populationMeansInAdmixture" ] }, { "page": "pquantile", "title": "Parallel quantile, median, mean", "topics": [ "pmean", "pmean.fromList", "pmedian", "pminWhich.fromList", "pquantile", "pquantile.fromList" ] }, { "page": "prepComma", "title": "Prepend a comma to a non-empty string", "topics": [ "prepComma" ] }, { "page": "prependZeros", "title": "Pad numbers with leading zeros to specified total width", "topics": [ "prependZeros", "prependZeros.int" ] }, { "page": "preservationNetworkConnectivity", "title": "Network preservation calculations", "topics": [ "preservationNetworkConnectivity" ] }, { "page": "projectiveKMeans", "title": "Projective K-means (pre-)clustering of expression data", "topics": [ "projectiveKMeans" ] }, { "page": "proportionsInAdmixture", "title": "Estimate the proportion of pure populations in an admixed population based on marker expression values.", "topics": [ "proportionsInAdmixture" ] }, { "page": "propVarExplained", "title": "Proportion of variance explained by eigengenes.", "topics": [ "propVarExplained" ] }, { "page": "pruneAndMergeConsensusModules", "title": "Iterative pruning and merging of (hierarchical) consensus modules", "topics": [ "pruneAndMergeConsensusModules" ] }, { "page": "pruneConsensusModules", "title": "Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity", "topics": [ "pruneConsensusModules" ] }, { "page": "PWLists", "title": "Pathways with Corresponding Gene Markers - Compiled by Mike Palazzolo and Jim Wang from CHDI", "topics": [ "PWLists" ] }, { "page": "qvalue", "title": "Estimate the q-values for a given set of p-values", "topics": [ "qvalue" ] }, { "page": "qvalue.restricted", "title": "qvalue convenience wrapper", "topics": [ "qvalue.restricted" ] }, { "page": "randIndex", "title": "Rand index of two partitions", "topics": [ "randIndex" ] }, { "page": "rankPvalue", "title": "Estimate the p-value for ranking consistently high (or low) on multiple lists", "topics": [ "rankPvalue" ] }, { "page": "recutBlockwiseTrees", "title": "Repeat blockwise module detection from pre-calculated data", "topics": [ "recutBlockwiseTrees" ] }, { "page": "recutConsensusTrees", "title": "Repeat blockwise consensus module detection from pre-calculated data", "topics": [ "recutConsensusTrees" ] }, { "page": "redWhiteGreen", "title": "Red-white-green color sequence", "topics": [ "redWhiteGreen" ] }, { "page": "relativeCorPredictionSuccess", "title": "Compare prediction success", "topics": [ "relativeCorPredictionSuccess" ] }, { "page": "removeGreyME", "title": "Removes the grey eigengene from a given collection of eigengenes.", "topics": [ "removeGreyME" ] }, { "page": "removePrincipalComponents", "title": "Remove leading principal components from data", "topics": [ "removePrincipalComponents" ] }, { "page": "replaceMissing", "title": "Replace missing values with a constant.", "topics": [ "replaceMissing" ] }, { "page": "returnGeneSetsAsList", "title": "Return pre-defined gene lists in several biomedical categories.", "topics": [ "returnGeneSetsAsList" ] }, { "page": "rgcolors.func", "title": "Red and Green Color Specification", "topics": [ "rgcolors.func" ] }, { "page": "sampledBlockwiseModules", "title": "Blockwise module identification in sampled data", "topics": [ "sampledBlockwiseModules" ] }, { "page": "sampledHierarchicalConsensusModules", "title": "Hierarchical consensus module identification in sampled data", "topics": [ "sampledHierarchicalConsensusModules" ] }, { "page": "scaleFreeFitIndex", "title": "Calculation of fitting statistics for evaluating scale free topology fit.", "topics": [ "scaleFreeFitIndex" ] }, { "page": "scaleFreePlot", "title": "Visual check of scale-free topology", "topics": [ "scaleFreePlot" ] }, { "page": "SCsLists", "title": "Stem Cell-Related Genes with Corresponding Gene Markers", "topics": [ "SCsLists" ] }, { "page": "selectFewestConsensusMissing", "title": "Select columns with the lowest consensus number of missing data", "topics": [ "selectFewestConsensusMissing" ] }, { "page": "setCorrelationPreservation", "title": "Summary correlation preservation measure", "topics": [ "setCorrelationPreservation" ] }, { "page": "shortenStrings", "title": "Shorten given character strings by truncating at a suitable separator.", "topics": [ "shortenStrings" ] }, { "page": "sigmoidAdjacencyFunction", "title": "Sigmoid-type adacency function.", "topics": [ "sigmoidAdjacencyFunction" ] }, { "page": "signedKME", "title": "Signed eigengene-based connectivity", "topics": [ "signedKME" ] }, { "page": "signifNumeric", "title": "Round numeric columns to given significant digits.", "topics": [ "signifNumeric" ] }, { "page": "signumAdjacencyFunction", "title": "Hard-thresholding adjacency function", "topics": [ "signumAdjacencyFunction" ] }, { "page": "simpleConsensusCalculation", "title": "Simple calculation of a single consenus", "topics": [ "simpleConsensusCalculation" ] }, { "page": "simpleHierarchicalConsensusCalculation", "title": "Simple hierarchical consensus calculation", "topics": [ "simpleHierarchicalConsensusCalculation" ] }, { "page": "simulateDatExpr", "title": "Simulation of expression data", "topics": [ "simulateDatExpr" ] }, { "page": "simulateDatExpr5Modules", "title": "Simplified simulation of expression data", "topics": [ "simulateDatExpr5Modules" ] }, { "page": "simulateEigengeneNetwork", "title": "Simulate eigengene network from a causal model", "topics": [ "simulateEigengeneNetwork" ] }, { "page": "simulateModule", "title": "Simulate a gene co-expression module", "topics": [ "simulateModule" ] }, { "page": "simulateMultiExpr", "title": "Simulate multi-set expression data", "topics": [ "simulateMultiExpr" ] }, { "page": "simulateSmallLayer", "title": "Simulate small modules", "topics": [ "simulateSmallLayer" ] }, { "page": "sizeGrWindow", "title": "Opens a graphics window with specified dimensions", "topics": [ "sizeGrWindow" ] }, { "page": "sizeRestrictedClusterMerge", "title": "Cluter merging with size restrictions", "topics": [ "sizeRestrictedClusterMerge" ] }, { "page": "softConnectivity", "title": "Calculates connectivity of a weighted network.", "topics": [ "softConnectivity", "softConnectivity.fromSimilarity" ] }, { "page": "spaste", "title": "Space-less paste", "topics": [ "spaste" ] }, { "page": "standardColors", "title": "Colors this library uses for labeling modules.", "topics": [ "standardColors" ] }, { "page": "standardScreeningBinaryTrait", "title": "Standard screening for binatry traits", "topics": [ "standardScreeningBinaryTrait" ] }, { "page": "standardScreeningCensoredTime", "title": "Standard Screening with regard to a Censored Time Variable", "topics": [ "standardScreeningCensoredTime" ] }, { "page": "standardScreeningNumericTrait", "title": "Standard screening for numeric traits", "topics": [ "standardScreeningNumericTrait" ] }, { "page": "stdErr", "title": "Standard error of the mean of a given vector.", "topics": [ "stdErr" ] }, { "page": "stratifiedBarplot", "title": "Bar plots of data across two splitting parameters", "topics": [ "stratifiedBarplot" ] }, { "page": "subsetTOM", "title": "Topological overlap for a subset of a whole set of genes", "topics": [ "subsetTOM" ] }, { "page": "swapTwoBranches", "title": "Select, swap, or reflect branches in a dendrogram.", "topics": [ "reflectBranch", "selectBranch", "swapTwoBranches" ] }, { "page": "TOMplot", "title": "Graphical representation of the Topological Overlap Matrix", "topics": [ "TOMplot" ] }, { "page": "TOMsimilarity", "title": "Topological overlap matrix similarity and dissimilarity", "topics": [ "TOMdist", "TOMsimilarity" ] }, { "page": "TOMsimilarityFromExpr", "title": "Topological overlap matrix", "topics": [ "TOMsimilarityFromExpr" ] }, { "page": "transposeBigData", "title": "Transpose a big matrix or data frame", "topics": [ "transposeBigData" ] }, { "page": "TrueTrait", "title": "Estimate the true trait underlying a list of surrogate markers.", "topics": [ "TrueTrait" ] }, { "page": "unsignedAdjacency", "title": "Calculation of unsigned adjacency", "topics": [ "unsignedAdjacency" ] }, { "page": "userListEnrichment", "title": "Measure enrichment between inputted and user-defined lists", "topics": [ "userListEnrichment" ] }, { "page": "vectorizeMatrix", "title": "Turn a matrix into a vector of non-redundant components", "topics": [ "vectorizeMatrix" ] }, { "page": "vectorTOM", "title": "Topological overlap for a subset of the whole set of genes", "topics": [ "vectorTOM" ] }, { "page": "verboseBarplot", "title": "Barplot with error bars, annotated by Kruskal-Wallis or ANOVA p-value", "topics": [ "verboseBarplot" ] }, { "page": "verboseBoxplot", "title": "Boxplot annotated by a Kruskal-Wallis p-value", "topics": [ "verboseBoxplot" ] }, { "page": "verboseIplot", "title": "Scatterplot with density", "topics": [ "verboseIplot" ] }, { "page": "verboseScatterplot", "title": "Scatterplot annotated by regression line and p-value", "topics": [ "verboseScatterplot" ] }, { "page": "votingLinearPredictor", "title": "Voting linear predictor", "topics": [ "votingLinearPredictor" ] } ], "_rundeps": [ "backports", "base64enc", "bslib", "cachem", "checkmate", "cli", "cluster", "codetools", "colorspace", "cpp11", "data.table", "digest", "doParallel", "dynamicTreeCut", "evaluate", "farver", "fastcluster", "fastmap", "fontawesome", "foreach", "foreign", "Formula", "fs", "ggplot2", "glue", "gridExtra", "gtable", "highr", "Hmisc", "htmlTable", "htmltools", "htmlwidgets", "impute", "isoband", "iterators", "jquerylib", "jsonlite", 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