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WGCNA (version 1.49)

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

11,673

Version

1.49

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

December 29th, 2015

Functions in WGCNA (1.49)

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

accuracyMeasures

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

Estimate the true trait underlying a list of surrogate markers.

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

Weights used in biweight midcovariance

Generalized Topological Overlap Measure

blockwiseModules

Automatic network construction and module detection

Add trait information to multi-set module eigengene structure

Add error bars to a barplot.

Show colors used to label modules

goodSamplesGenesMS

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

Add grid lines to an existing plot.

collapseRowsUsingKME

Selects one representative row per group based on kME

Calculation of biweight midcorrelations and associated p-values

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

corPvalueFisher

Fisher's asymptotic p-value for correlation

consensusProjectiveKMeans

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

Stem Cell-Related Genes with Corresponding Gene Markers

branchSplit.dissim

Branch split based on dissimilarity.

corPvalueStudent

Student asymptotic p-value for correlation

Topological overlap matrix similarity and dissimilarity

Constant-height tree cut

Calculate network adjacency

Biweight Midcorrelation

cutreeStaticColor

Constant height tree cut using color labels

Label scatterplot points

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

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

corPredictionSuccess

Qunatification of success of gene screening

Put close eigenvectors next to each other

Iterative garbage collection.

Graphical representation of the Topological Overlap Matrix

automaticNetworkScreening

One-step automatic network gene screening

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

exportNetworkToVisANT

Export network data in format readable by VisANT

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

Filter samples with too many missing entries across multiple data sets

dynamicMergeCut

Threshold for module merging

Visual check of scale-free topology

mergeCloseModules

Merge close modules in gene expression data

Relabel module labels to best match the given reference labels

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

coClustering.permutationTest

Permutation test for co-clustering

adjacency.polyReg

Adjacency matrix based on polynomial regression

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

Fixed-height cut of a dendrogram.

modulePreservation

Calculation of module preservation statistics

Apply a function to each set in a multiData structure.

propVarExplained

Proportion of variance explained by eigengenes.

Clustering coefficient calculation

networkScreening

Identification of genes related to a trait

fixDataStructure

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

qvalue.restricted

qvalue convenience wrapper

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

Red-white-green color sequence

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

Apply a function to elements of given multiData structures.

removePrincipalComponents

Remove leading principal components from data

Red and Green Color Image of Correlation Matrix

Weighted Gene Co-Expression Network Analysis

keepCommonProbes

Keep probes that are shared among given data sets

Divide tasks among workers

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

exportNetworkToCytoscape

Export network to Cytoscape

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Number of sets in a multi-set variable

Co-clustering measure of cluster preservation between two clusterings

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Align expression data with given vector

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

blockwiseConsensusModules

Find consensus modules across several datasets.

Blue-white-red color sequence

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

Color representation for a numeric variable

Check structure and retrieve sizes of a group of datasets.

branchSplitFromStabilityLabels

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

Select one representative row per group

Produce a labeled heatmap plot

Filter genes with too many missing entries

Fast calculations of Pearson correlation.

Break long character strings into multiple lines

chooseTopHubInEachModule

Chooses the top hub gene in each module

Subset rows and columns in a multiData structure

Consensus network (topological overlap).

Create a multiData structure.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

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

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

Meta-analysis of binary and continuous variables

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

Convert numerical labels to colors.

lowerTri2matrix

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

Simulate a gene co-expression module

simulateSmallLayer

Simulate small modules

moduleMergeUsingKME

Merge modules and reassign genes using kME.

Prepend a comma to a non-empty string

populationMeansInAdmixture

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

plotModuleSignificance

Barplot of module significance

Union and intersection of multiple sets

Red and Green Color Specification

Filter genes with too many missing entries across multiple sets

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

Barplot with text or color labels.

chooseOneHubInEachModule

Chooses a single hub gene in each module

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

matrixToNetwork

Construct a network from a matrix

Space-less paste

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

preservationNetworkConnectivity

Network preservation calculations

Calculation of correlations and associated p-values

multiData.eigengeneSignificance

Eigengene significance across multiple sets

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

Pad numbers with leading zeros to specified total width

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

correlationPreservation

Preservation of eigengene correlations

intramodularConnectivity

Calculation of intramodular connectivity

networkScreeningGS

Network gene screening with an external gene significance measure

Opens a graphics window with specified dimensions

normalizeLabels

Transform numerical labels into normal order.

Determine whether the supplied object is a valid multiData structure

networkConcepts

Calculations of network concepts

plotDendroAndColors

Dendrogram plot with color annotation of objects

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

mtd.setColnames

Get and set column names in a multiData structure.

Green-white-red color sequence

nearestCentroidPredictor

Nearest centroid predictor

Inline display of progress

Inline display of progress

Calculate overlap of modules

projectiveKMeans

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

Set attributes on each component of a multiData structure

Blood Cell Types with Corresponding Gene Markers

Calculate module eigengenes.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

Signed eigengene-based connectivity

Parallel quantile, median, mean

TOMsimilarityFromExpr

Topological overlap matrix

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

Pairwise scatterplots of eigengenes

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

standardScreeningNumericTrait

Standard screening for numeric traits

hubGeneSignificance

Hubgene significance

Brain-Related Categories with Corresponding Gene Markers

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

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

Meta-analysis Z statistic

Number of present data entries.

Removes the grey eigengene from a given collection of eigengenes.

simulateMultiExpr

Simulate multi-set expression data

simulateDatExpr5Modules

Simplified simulation of expression data

relativeCorPredictionSuccess

Compare prediction success

plotColorUnderTree

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

softConnectivity

Calculates connectivity of a weighted network.

simulateDatExpr

Simulation of expression data

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

plotNetworkHeatmap

Network heatmap plot

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

Filter samples with too many missing entries

Check adjacency matrix

unsignedAdjacency

Calculation of unsigned adjacency

stratifiedBarplot

Bar plots of data across two splitting parameters

standardScreeningBinaryTrait

Standard screening for binatry traits

Green-black-red color sequence

simulateEigengeneNetwork

Simulate eigengene network from a causal model

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

plotEigengeneNetworks

Eigengene network plot

nearestNeighborConnectivityMS

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

signumAdjacencyFunction

Hard-thresholding adjacency function

verboseScatterplot

Scatterplot annotated by regression line and p-value

proportionsInAdmixture

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

transposeBigData

Transpose a big matrix or data frame

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

Rand index of two partitions

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

votingLinearPredictor

Voting linear predictor

setCorrelationPreservation

Summary correlation preservation measure

moduleEigengenes

Calculate module eigengenes.

Topological overlap for a subset of the whole set of genes

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

overlapTableUsingKME

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

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

Boxplot annotated by a Kruskal-Wallis p-value

Topological overlap for a subset of a whole set of genes

Scatterplot with density

Red and Green Color Image of Data Matrix

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

Colors this library uses for labeling modules.

Standard error of the mean of a given vector.

Shorten given character strings by truncating at a suitable separator.

userListEnrichment

Measure enrichment between inputted and user-defined lists

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