Description Details Author(s) References

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.

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:

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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
``` |

Peter Langfelder <[email protected]> and Steve Horvath <[email protected]>, with contributions by Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang

Maintainer: Peter Langfelder <[email protected]>

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

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