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WGCNA (version 1.62)

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

16,020

Version

1.62

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

February 12th, 2018

Functions in WGCNA (1.62)

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

TOMsimilarityFromExpr

Topological overlap matrix

Estimate the true trait underlying a list of surrogate markers.

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

Align expression data with given vector

blockwiseConsensusModules

Find consensus modules across several datasets.

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

Check adjacency matrix

Check structure and retrieve sizes of a group of datasets.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

consensusCalculation

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

Consensus network (topological overlap).

Fast calculations of Pearson correlation.

Filter genes with too many missing entries

Filter genes with too many missing entries across multiple sets

Filter samples with too many missing entries

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Calculate individual correlation network matrices

Inline display of progress

Inline display of progress

matrixToNetwork

Construct a network from a matrix

mergeCloseModules

Merge close modules in gene expression data

modulePreservation

Calculation of module preservation statistics

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

Set attributes on each component of a multiData structure

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

nearestNeighborConnectivityMS

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

newBlockwiseData

Create, merge and expand BlockwiseData objects

Fixed-height cut of a dendrogram.

newConsensusOptions

Create a list holding consensus calculation options.

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

plotColorUnderTree

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

preservationNetworkConnectivity

Network preservation calculations

projectiveKMeans

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

Rand index of two partitions

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

Red-white-green color sequence

relativeCorPredictionSuccess

Compare prediction success

signumAdjacencyFunction

Hard-thresholding adjacency function

simpleConsensusCalculation

Simple calculation of a single consenus

softConnectivity

Calculates connectivity of a weighted network.

Space-less paste

transposeBigData

Transpose a big matrix or data frame

unsignedAdjacency

Calculation of unsigned adjacency

verboseScatterplot

Scatterplot annotated by regression line and p-value

votingLinearPredictor

Voting linear predictor

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

Stem Cell-Related Genes with Corresponding Gene Markers

Add error bars to a barplot.

Add grid lines to an existing plot.

Biweight Midcorrelation

Calculation of biweight midcorrelations and associated p-values

Select one representative row per group

collapseRowsUsingKME

Selects one representative row per group based on kME

exportNetworkToVisANT

Export network data in format readable by VisANT

fixDataStructure

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

hierarchicalConsensusCalculation

Hierarchical consensus calculation

hierarchicalConsensusKME

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

intramodularConnectivity

Calculation of intramodular connectivity

Determine whether the supplied object is a valid multiData structure

lowerTri2matrix

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

Relabel module labels to best match the given reference labels

moduleEigengenes

Calculate module eigengenes.

moduleMergeUsingKME

Merge modules and reassign genes using kME.

mtd.setColnames

Get and set column names in a multiData structure.

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

Number of sets in a multi-set variable

nearestCentroidPredictor

Nearest centroid predictor

automaticNetworkScreening

One-step automatic network gene screening

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

blockwiseModules

Automatic network construction and module detection

newConsensusTree

Create a new consensus tree

newCorrelationOptions

Creates a list of correlation options.

Red and Green Color Image of Correlation Matrix

plotDendroAndColors

Dendrogram plot with color annotation of objects

Plot multiple histograms in a single plot

plotNetworkHeatmap

Network heatmap plot

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

Blue-white-red color sequence

Clustering coefficient calculation

Co-clustering measure of cluster preservation between two clusterings

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

Replace missing values with a constant.

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

setCorrelationPreservation

Summary correlation preservation measure

Shorten given character strings by truncating at a suitable separator.

Standard error of the mean of a given vector.

stratifiedBarplot

Bar plots of data across two splitting parameters

corPvalueFisher

Fisher's asymptotic p-value for correlation

corPvalueStudent

Student asymptotic p-value for correlation

Break long character strings into multiple lines

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

goodSamplesGenesMS

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

Filter samples with too many missing entries across multiple data sets

hubGeneSignificance

Hubgene significance

Impute missing data separately in each module

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

Number of present data entries.

Color representation for a numeric variable

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

Calculate overlap of modules

overlapTableUsingKME

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

pruneAndMergeConsensusModules

Iterative pruning and merging of (hierarchical) consensus modules

pruneConsensusModules

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

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Signed eigengene-based connectivity

simulateSmallLayer

Simulate small modules

Opens a graphics window with specified dimensions

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

standardScreeningNumericTrait

Standard screening for numeric traits

Boxplot annotated by a Kruskal-Wallis p-value

Scatterplot with density

Various basic operations on BlockwiseData objects.

Weighted Gene Co-Expression Network Analysis

accuracyMeasures

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

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

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

Calculation of correlations and associated p-values

corPredictionSuccess

Qunatification of success of gene screening

Green-black-red color sequence

Green-white-red color sequence

hierarchicalConsensusTOM

Calculation of hierarchical consensus topological overlap matrix

hierarchicalMergeCloseModules

Merge close (similar) hierarchical consensus modules

Convert numerical labels to colors.

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

Meta-analysis of binary and continuous variables

Meta-analysis Z statistic

Subset rows and columns in a multiData structure

Create a multiData structure.

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Prepend a comma to a non-empty string

Pad numbers with leading zeros to specified total width

propVarExplained

Proportion of variance explained by eigengenes.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

proportionsInAdmixture

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

Red and Green Color Specification

sampledBlockwiseModules

Blockwise module identification in sampled data

simpleHierarchicalConsensusCalculation

Simple hierarchical consensus calculation

simulateDatExpr

Simulation of expression data

simulateDatExpr5Modules

Simplified simulation of expression data

simulateEigengeneNetwork

Simulate eigengene network from a causal model

Topological overlap for a subset of a whole set of genes

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

Generalized Topological Overlap Measure

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

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

Add trait information to multi-set module eigengene structure

Divide tasks among workers

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

branchSplit.dissim

Branch split based on dissimilarity.

branchSplitFromStabilityLabels

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

coClustering.permutationTest

Permutation test for co-clustering

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

Iterative garbage collection.

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

correlationPreservation

Preservation of eigengene correlations

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

Show colors used to label modules

dynamicMergeCut

Threshold for module merging

Produce a labeled heatmap plot

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

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

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

Calculate module eigengenes.

Union and intersection of multiple sets

networkConcepts

Calculations of network concepts

networkScreening

Identification of genes related to a trait

Put close eigenvectors next to each other

orderMEsByHierarchicalConsensus

Order module eigengenes by their hierarchical consensus similarity

populationMeansInAdmixture

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

Parallel quantile, median, mean

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

qvalue.restricted

qvalue convenience wrapper

sampledHierarchicalConsensusModules

Hierarchical consensus module identification in sampled data

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

Simulate a gene co-expression module

simulateMultiExpr

Simulate multi-set expression data

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

Blood Cell Types with Corresponding Gene Markers

Brain-Related Categories with Corresponding Gene Markers

Graphical representation of the Topological Overlap Matrix

Topological overlap matrix similarity and dissimilarity

Calculate network adjacency

adjacency.polyReg

Adjacency matrix based on polynomial regression

Weights used in biweight midcovariance

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

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

chooseOneHubInEachModule

Chooses a single hub gene in each module

chooseTopHubInEachModule

Chooses the top hub gene in each module

consensusProjectiveKMeans

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

consensusRepresentatives

Consensus selection of group representatives

Constant-height tree cut

cutreeStaticColor

Constant height tree cut using color labels

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

exportNetworkToCytoscape

Export network to Cytoscape

hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

hierarchicalConsensusModules

Hierarchical consensus network construction and module identification

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

keepCommonProbes

Keep probes that are shared among given data sets

Label scatterplot points

Barplot with text or color labels.

Apply a function to each set in a multiData structure.

Apply a function to elements of given multiData structures.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

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

networkScreeningGS

Network gene screening with an external gene significance measure

newBlockInformation

Create a list holding information about dividing data into blocks

newNetworkOptions

Create a list of network construction arguments (options).

normalizeLabels

Transform numerical labels into normal order.

plotEigengeneNetworks

Eigengene network plot

Pairwise scatterplots of eigengenes

Red and Green Color Image of Data Matrix

plotModuleSignificance

Barplot of module significance

Removes the grey eigengene from a given collection of eigengenes.

removePrincipalComponents

Remove leading principal components from data

Visual check of scale-free topology

selectFewestConsensusMissing

Select columns with the lowest consensus number of missing data

Colors this library uses for labeling modules.

standardScreeningBinaryTrait

Standard screening for binatry traits

userListEnrichment

Measure enrichment between inputted and user-defined lists

Topological overlap for a subset of the whole set of genes