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WGCNA (version 1.64-1)

Weighted Correlation Network Analysis

Description

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . 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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Install

install.packages('WGCNA')

Monthly Downloads

11,673

Version

1.64-1

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

September 11th, 2018

Functions in WGCNA (1.64-1)

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

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

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Blue-white-red color sequence

Generalized Topological Overlap Measure

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

Select one representative row per group

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

Add trait information to multi-set module eigengene structure

accuracyMeasures

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

Calculate network adjacency

Add error bars to a barplot.

Calculation of biweight midcorrelations and associated p-values

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

Weights used in biweight midcovariance

blockwiseModules

Automatic network construction and module detection

Align expression data with given vector

Various basic operations on BlockwiseData objects.

coClustering.permutationTest

Permutation test for co-clustering

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

Co-clustering measure of cluster preservation between two clusterings

corPvalueFisher

Fisher's asymptotic p-value for correlation

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

Graphical representation of the Topological Overlap Matrix

corPvalueStudent

Student asymptotic p-value for correlation

TOMsimilarityFromExpr

Topological overlap matrix

Divide tasks among workers

Estimate the true trait underlying a list of surrogate markers.

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

binarizeCategoricalColumns

Turn categorical columns into sets of binary indicators

Topological overlap matrix similarity and dissimilarity

consensusCalculation

Calculation of a (single) consenus with optional data calibration.

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

adjacency.polyReg

Adjacency matrix based on polynomial regression

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

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

consensusRepresentatives

Consensus selection of group representatives

blockwiseConsensusModules

Find consensus modules across several datasets.

Consensus network (topological overlap).

binarizeCategoricalVariable

Turn a categorical variable into a set of binary indicators

exportNetworkToCytoscape

Export network to Cytoscape

chooseTopHubInEachModule

Chooses the top hub gene in each module

branchSplitFromStabilityLabels

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

Show colors used to label modules

Clustering coefficient calculation

collapseRowsUsingKME

Selects one representative row per group based on kME

Impute missing data separately in each module

Iterative garbage collection.

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

dynamicMergeCut

Threshold for module merging

Check adjacency matrix

automaticNetworkScreening

One-step automatic network gene screening

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

correlationPreservation

Preservation of eigengene correlations

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

Calculate individual correlation network matrices

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

branchSplit.dissim

Branch split based on dissimilarity.

Convert numerical labels to colors.

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

Filter genes with too many missing entries

exportNetworkToVisANT

Export network data in format readable by VisANT

factorizeNonNumericColumns

Turn non-numeric columns into factors

Green-white-red color sequence

consensusProjectiveKMeans

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

Filter samples with too many missing entries across multiple data sets

Green-black-red color sequence

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

Determine whether the supplied object is a valid multiData structure

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

hierarchicalConsensusCalculation

Hierarchical consensus calculation

Check structure and retrieve sizes of a group of datasets.

moduleEigengenes

Calculate module eigengenes.

Apply a function to elements of given multiData structures.

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

chooseOneHubInEachModule

Chooses a single hub gene in each module

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

nearestCentroidPredictor

Nearest centroid predictor

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

hubGeneSignificance

Hubgene significance

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

moduleMergeUsingKME

Merge modules and reassign genes using kME.

Meta-analysis Z statistic

keepCommonProbes

Keep probes that are shared among given data sets

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

Blood Cell Types with Corresponding Gene Markers

Label scatterplot points

mergeCloseModules

Merge close modules in gene expression data

Brain-Related Categories with Corresponding Gene Markers

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

Fixed-height cut of a dendrogram.

Fast joint calculation of row- or column-wise minima and indices of minimum elements

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

Create a multiData structure.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

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

Biweight Midcorrelation

newCorrelationOptions

Creates a list of correlation options.

newNetworkOptions

Create a list of network construction arguments (options).

Union and intersection of multiple sets

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

convertNumericColumnsToNumeric

Convert character columns that represent numbers to numeric

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

Put close eigenvectors next to each other

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

Meta-analysis of binary and continuous variables

Calculation of correlations and associated p-values

Number of present data entries.

plotDendroAndColors

Dendrogram plot with color annotation of objects

Subset rows and columns in a multiData structure

corPredictionSuccess

Qunatification of success of gene screening

Number of sets in a multi-set variable

Fast calculations of Pearson correlation.

Constant-height tree cut

plotEigengeneNetworks

Eigengene network plot

Pairwise scatterplots of eigengenes

Pad numbers with leading zeros to specified total width

networkScreening

Identification of genes related to a trait

networkScreeningGS

Network gene screening with an external gene significance measure

preservationNetworkConnectivity

Network preservation calculations

cutreeStaticColor

Constant height tree cut using color labels

newConsensusOptions

Create a list holding consensus calculation options.

newBlockInformation

Create a list holding information about dividing data into blocks

newBlockwiseData

Create, merge and expand BlockwiseData objects

fixDataStructure

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

Break long character strings into multiple lines

Filter genes with too many missing entries across multiple sets

newConsensusTree

Create a new consensus tree

qvalue.restricted

qvalue convenience wrapper

Filter samples with too many missing entries

Red and Green Color Image of Data Matrix

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

Calculate overlap of modules

plotModuleSignificance

Barplot of module significance

hierarchicalConsensusKME

Calculation of measures of fuzzy module membership (KME) in hierarchical consensus modules

Plot multiple histograms in a single plot

Rand index of two partitions

Signed eigengene-based connectivity

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

Round numeric columns to given significant digits.

signumAdjacencyFunction

Hard-thresholding adjacency function

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

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

lowerTri2matrix

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

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

simpleConsensusCalculation

Simple calculation of a single consenus

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

projectiveKMeans

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

Set attributes on each component of a multiData structure

Visual check of scale-free topology

goodSamplesGenesMS

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

Inline display of progress

Inline display of progress

propVarExplained

Proportion of variance explained by eigengenes.

mtd.setColnames

Get and set column names in a multiData structure.

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Analogs of grep(l) and (g)sub for multiple patterns and relacements

relativeCorPredictionSuccess

Compare prediction success

softConnectivity

Calculates connectivity of a weighted network.

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

intramodularConnectivity

Calculation of intramodular connectivity

Removes the grey eigengene from a given collection of eigengenes.

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

plotNetworkHeatmap

Network heatmap plot

Shorten given character strings by truncating at a suitable separator.

populationMeansInAdmixture

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

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Barplot with text or color labels.

Produce a labeled heatmap plot

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Calculate module eigengenes.

Relabel module labels to best match the given reference labels

Space-less paste

verboseScatterplot

Scatterplot annotated by regression line and p-value

Red-white-green color sequence

plotColorUnderTree

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

votingLinearPredictor

Voting linear predictor

pruneConsensusModules

Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity

Red and Green Color Image of Correlation Matrix

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

standardScreeningNumericTrait

Standard screening for numeric traits

transposeBigData

Transpose a big matrix or data frame

sampledBlockwiseModules

Blockwise module identification in sampled data

unsignedAdjacency

Calculation of unsigned adjacency

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

Simulate a gene co-expression module

simulateMultiExpr

Simulate multi-set expression data

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

simulateDatExpr5Modules

Simplified simulation of expression data

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Red and Green Color Specification

matrixToNetwork

Construct a network from a matrix

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

setCorrelationPreservation

Summary correlation preservation measure

Topological overlap for a subset of a whole set of genes

modulePreservation

Calculation of module preservation statistics

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

Apply a function to each set in a multiData structure.

networkConcepts

Calculations of network concepts

nearestNeighborConnectivityMS

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

simulateDatExpr

Simulation of expression data

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

normalizeLabels

Transform numerical labels into normal order.

Color representation for a numeric variable

overlapTableUsingKME

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

userListEnrichment

Measure enrichment between inputted and user-defined lists

Colors this library uses for labeling modules.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

Parallel quantile, median, mean

Topological overlap for a subset of the whole set of genes

Prepend a comma to a non-empty string

proportionsInAdmixture

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

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

removePrincipalComponents

Remove leading principal components from data

Standard error of the mean of a given vector.

stratifiedBarplot

Bar plots of data across two splitting parameters

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

Replace missing values with a constant.

simulateSmallLayer

Simulate small modules

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

standardScreeningBinaryTrait

Standard screening for binatry traits

Opens a graphics window with specified dimensions

Boxplot annotated by a Kruskal-Wallis p-value

Scatterplot with density

Stem Cell-Related Genes with Corresponding Gene Markers

Add grid lines to an existing plot.