WGCNA-package: Weighted Gene Co-Expression Network Analysis

Description

Functions necessary to perform Weighted Correlation Network Analysis. WGCNA is also known as weighted gene co-expression network analysis when dealing with gene expression data. Many functions of WGCNA can also be used for general association networks specified by a symmetric adjacency matrix.

Arguments

Details

Package:	WGCNA
Version:	1.51
Date:	2016-03-08
Depends:	R (>= 3.0), dynamicTreeCut (>= 1.62), fastcluster, Hmisc
Imports:	stats, impute, grDevices, utils, splines, reshape, foreach, doParallel, matrixStats (>= 0.8.1), GO.db, AnnotationDbi
Suggests:	org.Hs.eg.db, org.Mm.eg.db, infotheo, entropy, minet, survival
ZipData:	no
License:	GPL (>= 2)
URL:	http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA/

Index:

GTOMdist Generalized Topological Overlap Measure TOMdist Topological overlap matrix dissimilarity TOMplot Graphical representation of the Topological Overlap Matrix TOMsimilarity Topological overlap matrix similarity TOMsimilarityFromExpr Topological overlap matrix similarity WGCNA-package Weighted Gene Co-Expression Network Analysis accuracyMeasures Accuracy measures for a 2x2 confusion matrix 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 from a similarity matrix adjacency.polyReg Adjacency based on polynomial regression adjacency.splineReg Adjacency based on natural cubic spline regression alignExpr Align expression data with given vector automaticNetworkScreening One-step automatic network gene screening automaticNetworkScreeningGS One-step automatic network gene screening with external gene significance AFcorMI Prediction of weighted mutual information adjacency matrix by correlation bicor Biweight Midcorrelation bicorAndPvalue Biweight Midcorrelation and the associated p-value blockwiseConsensusModules Find consensus modules across several datasets. blockwiseIndividualTOMs Calculate individual topological overlaps across multi-set data blockwiseModules Automatic network construction and module detection BloodLists (data) Gene sets for user enrichment analysis blueWhiteRed Blue-white-red color sequence BrainLists (data) Gene sets for user enrichment analysis BrainRegionMarkers (data) Gene Markers for Regions of the Human Brain checkAdjMat Check adjacency matrix checkSets Check structure and retrieve sizes of a group of datasets checkSimilarity Check a similarity matrix chooseOneHubInEachModule Choose a hub gene in each module chooseTopHubInEachModule Choose the top hub gene in each module clusterCoef Clustering coefficient calculation coClustering Cluster preservation based on co-clustering coClustering.permutationTest Permutation test for co-clustering collapseRows Collapse Rows in Numeric Matrix collapseRowsUsingKME Selects one representative row per group based on kM collectGarbage Iterative garbage collection colQuantileC Fast colunm-wise quantile of a matrix conformityBasedNetworkConcepts Calculation of conformity-based network concepts conformityDecomposition Conformity vector(s) and factorizability measure(s) of a network consensusDissTOMandTree Consensus TOM-based dissimilarity and clustering tree consensusKME Consensus eigengene-based connectivity consensusMEDissimilarity Consensus dissimilarity of module eigengenes. consensusOrderMEs Put close eigenvectors next to each other in several sets. consensusProjectiveKMeans Consensus projective K-means (pre-)clustering of expression data cor Faster calculation of Pearson correlations corAndPvalue Correlation and the associated p-value cor1 Faster calculation of column correlations of a matrix corFast Faster calculation of Pearson correlations corPredictionSuccess ~~function to do ... ~~ corPvalueFisher Fisher's asymptotic p-value for correlation corPvalueStudent Student asymptotic p-value for correlation 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 displayColors Show colors used to label modules dynamicMergeCut Threshold for module merging exportNetworkToVisANT Export network data in format readable by VisANT exportNetworkToCytoscape Export network data in format readable by Cytoscape fixDataStructure Put single-set data into a form useful for multiset calculations fundamentalNetworkConcepts Calculation of fundamental network concepts GOenrichmentAnalysis Calculate enrichment p-values of clusters in GO terms 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 hubGeneSignificance Hubgene significance ImmunePathwayLists (data) Immune Pathways with Corresponding Gene Markers initProgInd Inline display of progress intramodularConnectivity intramodularConnectivity.fromExpr Calculation of intramodular connectivity keepCommonProbes Keep probes that are shared among given data sets kMEcomparisonScatterplot Scatterplots of eigengene-based connectivity labeledBarplot Barplot with text or color labels labeledHeatmap Produce a labeled heatmap plot labelPoints Attempt to intelligently label points in a scatterplot labels2colors Convert numerical labels to colors lowerTri2matrix Reconstruct a symmetric matrix from a distance (lower-triangular) representation matchLabels Relabel modules to best approximate a reference labeling mergeCloseModules Merge close modules in gene expression data metaAnalysis Meta-analysis significance statistics metaZfunction Meta-analysis Z statistic 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 multiSetMEs Calculate module eigengenes multiData.eigengeneSignificance Calculate eigengene significance for multiple data sets mutualInfoAdjacency Calculate weighted adjacency matrices based on mutual information nPresent Number of present data entries nearestNeighborConnectivity Connectivity to a constant number of nearest neighbors nearestNeighborConnectivityMS Connectivity to a constant number of nearest neighbors across multiple data sets nearestCentroidPredictor Nearest centroid predictor for two-class problems networkConcepts Calculations of network concepts networkScreening Network screening networkScreeningGS Network screening with external gene significance normalizeLabels Transform numerical labels into normal order numbers2colors Color representation for a numeric variable orderBranchesUsingHubGenes Optimize dendrogram using branch swaps and reflections orderMEs Put close eigenvectors next to each other overlapTable Overlap counts and Fisher exact tests for two sets of module labels overlapTableUsingKME Determines significant overlap between modules in two networks based on kME tables 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 under a dendrogram plotDendroAndColors Dendrogram plot with color annotation of objects plotEigengeneNetworks Eigengene network plot plotMEpairs Pairwise scatterplots of eigengenes plotModuleSignificance Barplot of module significance plotNetworkHeatmap Network heatmap plot pmean Parallel mean pmedian Parallel median populationMeansInAdmixture Estimation of population-specific mean values in an admixed population pquantile Parallel quantile preservationNetworkConnectivity Network preservation calculations projectiveKMeans Projective K-means (pre-)clustering of expression data propVarExplained Proportion of variance explained by eigengenes proportionsInAdmixture Estimation of proportion of pure populations in an admixture qvalue q-value calculation from package qvalue randIndex Calculation of (adjusted) Rand index randomGLMpredictor Ensemble predictor based on bagging of generalized linear models 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 reflectTwoBranches 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 returnGeneSetsAsLists Return pre-defined gene lists in several biomedical categories. scaleFreeFitIndex Calculation of fitting statistics for evaluating scale free topology fit. scaleFreePlot Visual check of scale-free topology SCsLists (data) Stem Cell-Related Genes with Corresponding Gene Markers selectBranch Find a branch in a dendrogram setCorrelationPreservation Summary correlation preservation measure sigmoidAdjacencyFunction Sigmoid-type adacency function signedKME Signed eigengene-based connectivity signumAdjacencyFunction Hard-thresholding adjacency function simulateDatExpr Simulation of expression data simulateDatExpr5Modules

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 Open a graphics window of given width and height softConnectivity Calculation of soft (weighted) connectevity softConnectivity.fromSimilarity Calculation of soft (weighted) connectevity spaste Space-less paste standardColors Colors this library uses for labeling modules standardScreeningBinaryTrait Standard screening for a binary trait standardScreeningCensoredTime Standard screening with regard to a Censored Time Variable stdErr Standard error stratifiedBarplot Bar plots of data across two splitting parameters swapTwoBranches Swap branches in a dendrogram TrueTrait Estimate the true trait underlying a list of surrogate markers transposeBigData Block-by-block transpose of large matrices unsignedAdjacency Calculation of unsigned adjacency userListEnrichment Measure enrichment between inputted and user-defined lists vectorTOM Topological overlap for a subset of the whole set of genes vectorizeMatrix Turn a matrix into a vector of non-redundant components verboseBarplot Barplot with error bars, annotated by Kruskal-Wallis p-value verboseBoxplot Boxplot annotated by a Kruskal-Wallis p-value verboseScatterplot Scatterplot annotated by regression line and p-value verboseIplot Scatterplot annotated by regression line, p-value, and color for density votingLinearPredictor Voting linear predictor

References

Peter Langfelder and Steve Horvath (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559

Peter Langfelder, Steve Horvath (2012) Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. http://www.jstatsoft.org/v46/i11/

Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17

Dong J, Horvath S (2007) Understanding Network Concepts in Modules, BMC Systems Biology 2007, 1:24

Horvath S, Dong J (2008) Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Comput Biol 4(8): e1000117

Yip A, Horvath S (2007) Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics 2007, 8:22

Langfelder P, Zhang B, Horvath S (2007) Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut library for R. Bioinformatics. November/btm563

Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology 2007, 1:54