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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Details
| Date | 2019-05-22 |
| LinkingTo | Rcpp |
| ZipData | no |
| License | GPL (>= 2) |
| URL | http://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/ |
| NeedsCompilation | yes |
| Packaged | 2019-05-22 23:44:43 UTC; plangfelder |
| Repository | CRAN |
| Date/Publication | 2019-05-23 05:10:03 UTC |
| imports | AnnotationDbi , doParallel , foreach , GO.db , grDevices , Hmisc , impute , matrixStats (>= 0.8.1) , parallel , preprocessCore , Rcpp (>= 0.11.0) , robust , splines , stats , survival , utils |
| depends | dynamicTreeCut (>= 1.62) , fastcluster , R (>= 3.0) |
| suggests | entropy , infotheo , minet , org.Hs.eg.db , org.Mm.eg.db |
| Contributors | Steve Horvath, Peter Langfelder |
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