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WGCNA (version 1.51)

Weighted Correlation Network Analysis

Description

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. 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.

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Version

Install

install.packages('WGCNA')

Monthly Downloads

13,606

Version

1.51

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

March 15th, 2016

Functions in WGCNA (1.51)

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

Graphical representation of the Topological Overlap Matrix

Blue-white-red color sequence

Clustering coefficient calculation

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

branchSplit.dissim

Branch split based on dissimilarity.

Stem Cell-Related Genes with Corresponding Gene Markers

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

blockwiseModules

Automatic network construction and module detection

Inline display of progress

Inline display of progress

Weighted Gene Co-Expression Network Analysis

Calculate network adjacency

Add vertical ``guide lines'' to a dendrogram plot

Visual check of scale-free topology

Pathways with Corresponding Gene Markers - Compiled by Mike Palazzolo and Jim Wang from CHDI

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

consensusProjectiveKMeans

Consensus projective K-means (pre-)clustering of expression data

Calculation of biweight midcorrelations and associated p-values

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

adjacency.polyReg

Adjacency matrix based on polynomial regression

Produce a labeled heatmap plot

setCorrelationPreservation

Summary correlation preservation measure

Fixed-height cut of a dendrogram.

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

Filter samples with too many missing entries across multiple data sets

Brain-Related Categories with Corresponding Gene Markers

Label scatterplot points

Show colors used to label modules

Fast colunm- and row-wise quantile of a matrix.

goodSamplesGenesMS

Iterative filtering of samples and genes with too many missing entries across multiple data sets

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

Attempt to calculate an appropriate block size to maximize efficiency of block-wise calcualtions.

collapseRowsUsingKME

Selects one representative row per group based on kME

hubGeneSignificance

Hubgene significance

Relabel module labels to best match the given reference labels

accuracyMeasures

Accuracy measures for a 2x2 confusion matrix or for vectors of predicted and observed values.

Meta-analysis of binary and continuous variables

automaticNetworkScreening

One-step automatic network gene screening

Filter genes with too many missing entries across multiple sets

Convert a list to a multiData structure and vice-versa.

Generalized Topological Overlap Measure

Apply a function to elements of given multiData structures.

mergeCloseModules

Merge close modules in gene expression data

If possible, simplify a multiData structure to a 3-dimensional array.

Weights used in biweight midcovariance

Check adjacency matrix

Set attributes on each component of a multiData structure

Create a multiData structure.

corPvalueFisher

Fisher's asymptotic p-value for correlation

Calculate consensus kME (eigengene-based connectivities) across multiple data sets.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

Red and Green Color Specification

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Biweight Midcorrelation

matrixToNetwork

Construct a network from a matrix

Number of sets in a multi-set variable

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

exportNetworkToVisANT

Export network data in format readable by VisANT

Estimate the q-values for a given set of p-values

plotEigengeneNetworks

Eigengene network plot

Add grid lines to an existing plot.

chooseTopHubInEachModule

Chooses the top hub gene in each module

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

qvalue.restricted

qvalue convenience wrapper

correlationPreservation

Preservation of eigengene correlations

coClustering.permutationTest

Permutation test for co-clustering

Constant-height tree cut

chooseOneHubInEachModule

Chooses a single hub gene in each module

Align expression data with given vector

blockwiseConsensusModules

Find consensus modules across several datasets.

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

Topological overlap matrix similarity and dissimilarity

Estimate the true trait underlying a list of surrogate markers.

Divide tasks among workers

corPvalueStudent

Student asymptotic p-value for correlation

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

moduleMergeUsingKME

Merge modules and reassign genes using kME.

Scatterplot with density

Determine whether the supplied object is a valid multiData structure

intramodularConnectivity

Calculation of intramodular connectivity

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Meta-analysis Z statistic

Co-clustering measure of cluster preservation between two clusterings

Filter samples with too many missing entries

Iterative garbage collection.

Select one representative row per group

branchSplitFromStabilityLabels

Branch split (dissimilarity) statistic derived from labels determined from a stability study

nearestNeighborConnectivityMS

Connectivity to a constant number of nearest neighbors across multiple data sets

Calculation of correlations and associated p-values

Pad numbers with leading zeros to specified total width

Check structure and retrieve sizes of a group of datasets.

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

consensusRepresentatives

Consensus selection of group representatives

unsignedAdjacency

Calculation of unsigned adjacency

corPredictionSuccess

Qunatification of success of gene screening

nearestCentroidPredictor

Nearest centroid predictor

Filter genes with too many missing entries

Blood Cell Types with Corresponding Gene Markers

Fast calculations of Pearson correlation.

Green-white-red color sequence

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

networkScreening

Identification of genes related to a trait

simulateEigengeneNetwork

Simulate eigengene network from a causal model

mtd.setColnames

Get and set column names in a multiData structure.

plotDendroAndColors

Dendrogram plot with color annotation of objects

networkConcepts

Calculations of network concepts

preservationNetworkConnectivity

Network preservation calculations

Red and Green Color Image of Data Matrix

Barplot with text or color labels.

Calculate overlap of modules

modulePreservation

Calculation of module preservation statistics

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

simulateDatExpr5Modules

Simplified simulation of expression data

Add error bars to a barplot.

Rand index of two partitions

moduleEigengenes

Calculate module eigengenes.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

Turn a multiData structure into a single matrix or data frame.

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

Green-black-red color sequence

populationMeansInAdmixture

Estimate the population-specific mean values in an admixed population.

plotColorUnderTree

Plot color rows in a given order, for example under a dendrogram

verboseScatterplot

Scatterplot annotated by regression line and p-value

Subset rows and columns in a multiData structure

Number of present data entries.

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

transposeBigData

Transpose a big matrix or data frame

Add trait information to multi-set module eigengene structure

networkScreeningGS

Network gene screening with an external gene significance measure

TOMsimilarityFromExpr

Topological overlap matrix

Shorten given character strings by truncating at a suitable separator.

standardScreeningBinaryTrait

Standard screening for binatry traits

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

Topological overlap for a subset of the whole set of genes

cutreeStaticColor

Constant height tree cut using color labels

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

keepCommonProbes

Keep probes that are shared among given data sets

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

Consensus network (topological overlap).

Convert numerical labels to colors.

Prepend a comma to a non-empty string

proportionsInAdmixture

Estimate the proportion of pure populations in an admixed population based on marker expression values.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

Red and Green Color Image of Correlation Matrix

relativeCorPredictionSuccess

Compare prediction success

overlapTableUsingKME

Determines significant overlap between modules in two networks based on kME tables.

plotNetworkHeatmap

Network heatmap plot

Parallel quantile, median, mean

propVarExplained

Proportion of variance explained by eigengenes.

exportNetworkToCytoscape

Export network to Cytoscape

stratifiedBarplot

Bar plots of data across two splitting parameters

Space-less paste

softConnectivity

Calculates connectivity of a weighted network.

projectiveKMeans

Projective K-means (pre-)clustering of expression data

plotModuleSignificance

Barplot of module significance

Break long character strings into multiple lines

normalizeLabels

Transform numerical labels into normal order.

Standard error of the mean of a given vector.

simulateDatExpr

Simulation of expression data

Calculate module eigengenes.

Union and intersection of multiple sets

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Removes the grey eigengene from a given collection of eigengenes.

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

Put close eigenvectors next to each other

Red-white-green color sequence

Estimate the p-value for ranking consistently high (or low) on multiple lists

Signed eigengene-based connectivity

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

dynamicMergeCut

Threshold for module merging

signumAdjacencyFunction

Hard-thresholding adjacency function

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

simulateMultiExpr

Simulate multi-set expression data

fixDataStructure

Put single-set data into a form useful for multiset calculations.

Barplot with error bars, annotated by Kruskal-Wallis or ANOVA p-value

simulateSmallLayer

Simulate small modules

Color representation for a numeric variable

Boxplot annotated by a Kruskal-Wallis p-value

Topological overlap for a subset of a whole set of genes

lowerTri2matrix

Reconstruct a symmetric matrix from a distance (lower-triangular) representation

removePrincipalComponents

Remove leading principal components from data

Pairwise scatterplots of eigengenes

Opens a graphics window with specified dimensions

votingLinearPredictor

Voting linear predictor

Simulate a gene co-expression module

Colors this library uses for labeling modules.

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

userListEnrichment

Measure enrichment between inputted and user-defined lists

Apply a function to each set in a multiData structure.

standardScreeningNumericTrait

Standard screening for numeric traits