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WGCNA (version 1.47)

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

1.47

License

GPL (>= 2)

Maintainer

Peter Langfelder

Last Published

June 15th, 2015

Functions in WGCNA (1.47)

Add grid lines to an existing plot.

Estimate the true trait underlying a list of surrogate markers.

collapseRowsUsingKME

Selects one representative row per group based on kME

Clustering coefficient calculation

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.

Iterative garbage collection.

conformityBasedNetworkConcepts

Calculation of conformity-based network concepts.

blockwiseConsensusModules

Find consensus modules across several datasets.

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

coClustering.permutationTest

Permutation test for co-clustering

consensusDissTOMandTree

Consensus clustering based on topological overlap and hierarchical clustering

Stem Cell-Related Genes with Corresponding Gene Markers

Meta-analysis of binary and continuous variables

Calculate network adjacency

moduleEigengenes

Calculate module eigengenes.

Graphical representation of the Topological Overlap Matrix

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

goodSamplesGenes

Iterative filtering of samples and genes with too many missing entries

Add trait information to multi-set module eigengene structure

Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

GOenrichmentAnalysis

Calculation of GO enrichment (experimental)

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

keepCommonProbes

Keep probes that are shared among given data sets

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

Weights used in biweight midcovariance

adjacency.splineReg

Calculate network adjacency based on natural cubic spline regression

exportNetworkToVisANT

Export network data in format readable by VisANT

BrainRegionMarkers

Gene Markers for Regions of the Human Brain

mtd.setColnames

Get and set column names in a multiData structure.

corPredictionSuccess

Qunatification of success of gene screening

Generalized Topological Overlap Measure

hubGeneSignificance

Hubgene significance

chooseOneHubInEachModule

Chooses a single hub gene in each module

adjacency.polyReg

Adjacency matrix based on polynomial regression

blockwiseModules

Automatic network construction and module detection

chooseTopHubInEachModule

Chooses the top hub gene in each module

mergeCloseModules

Merge close modules in gene expression data

Topological overlap matrix similarity and dissimilarity

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

Weighted Gene Co-Expression Network Analysis

allowWGCNAThreads

Allow and disable multi-threading for certain WGCNA calculations

correlationPreservation

Preservation of eigengene correlations

Blood Cell Types with Corresponding Gene Markers

Biweight Midcorrelation

networkConcepts

Calculations of network concepts

Calculation of biweight midcorrelations and associated p-values

coxRegressionResiduals

Deviance- and martingale residuals from a Cox regression model

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

branchSplitFromStabilityLabels

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

Co-clustering measure of cluster preservation between two clusterings

fundamentalNetworkConcepts

Calculation of fundamental network concepts from an adjacency matrix.

Select one representative row per group

corPvalueFisher

Fisher's asymptotic p-value for correlation

TOMsimilarityFromExpr

Topological overlap matrix

Shorten given character strings by truncating at a suitable separator.

Brain-Related Categories with Corresponding Gene Markers

empiricalBayesLM

Empirical Bayes-moderated adjustment for unwanted covariates

Divide tasks among workers

Break long character strings into multiple lines

Green-white-red color sequence

consensusProjectiveKMeans

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

consensusOrderMEs

Put close eigenvectors next to each other in several sets.

Check adjacency matrix

Add error bars to a barplot.

plotNetworkHeatmap

Network heatmap plot

Red-white-green color sequence

Align expression data with given vector

Convert numerical labels to colors.

automaticNetworkScreeningGS

One-step automatic network gene screening with external gene significance

cutreeStaticColor

Constant height tree cut using color labels

matrixToNetwork

Construct a network from a matrix

Calculate module eigengenes.

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

fixDataStructure

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

softConnectivity

Calculates connectivity of a weighted network.

standardScreeningBinaryTrait

Standard screening for binatry traits

Check structure and retrieve sizes of a group of datasets.

nearestNeighborConnectivity

Connectivity to a constant number of nearest neighbors

overlapTableUsingKME

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

Calculation of correlations and associated p-values

corPvalueStudent

Student asymptotic p-value for correlation

Consensus network (topological overlap).

labeledHeatmap.multiPage

Labeled heatmap divided into several separate plots.

Filter genes with too many missing entries across multiple sets

Standard error of the mean of a given vector.

Colors this library uses for labeling modules.

Determine whether the supplied object is a valid multiData structure

nearestCentroidPredictor

Nearest centroid predictor

Rand index of two partitions

consensusMEDissimilarity

Consensus dissimilarity of module eigengenes.

plotModuleSignificance

Barplot of module significance

nearestNeighborConnectivityMS

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

standardScreeningCensoredTime

Standard Screening with regard to a Censored Time Variable

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

Parallel quantile, median, mean

Filter samples with too many missing entries across multiple data sets

automaticNetworkScreening

One-step automatic network gene screening

moduleMergeUsingKME

Merge modules and reassign genes using kME.

Show colors used to label modules

Red and Green Color Image of Correlation Matrix

blockwiseIndividualTOMs

Calculation of block-wise topological overlaps

simulateMultiExpr

Simulate multi-set expression data

Blue-white-red color sequence

simulateDatExpr

Simulation of expression data

branchSplit.dissim

Branch split based on dissimilarity.

branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Label scatterplot points

simulateSmallLayer

Simulate small modules

Filter samples with too many missing entries

dynamicMergeCut

Threshold for module merging

orderBranchesUsingHubGenes

Optimize dendrogram using branch swaps and reflections.

exportNetworkToCytoscape

Export network to Cytoscape

Inline display of progress

Inline display of progress

kMEcomparisonScatterplot

Function to plot kME values between two comparable data sets.

setCorrelationPreservation

Summary correlation preservation measure

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

ImmunePathwayLists

Immune Pathways with Corresponding Gene Markers

removePrincipalComponents

Remove leading principal components from data

Color representation for a numeric variable

Removes the grey eigengene from a given collection of eigengenes.

conformityDecomposition

Conformity and module based decomposition of a network adjacency matrix.

Number of sets in a multi-set variable

votingLinearPredictor

Voting linear predictor

Create a multiData structure.

pickHardThreshold

Analysis of scale free topology for hard-thresholding.

Produce a labeled heatmap plot

Apply a function to elements of given multiData structures.

multiData.eigengeneSignificance

Eigengene significance across multiple sets

networkScreening

Identification of genes related to a trait

Filter genes with too many missing entries

pickSoftThreshold

Analysis of scale free topology for soft-thresholding

Subset rows and columns in a multiData structure

Constant-height tree cut

Topological overlap for a subset of a whole set of genes

populationMeansInAdmixture

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

preservationNetworkConnectivity

Network preservation calculations

Fixed-height cut of a dendrogram.

Meta-analysis Z statistic

plotEigengeneNetworks

Eigengene network plot

Set attributes on each component of a multiData structure

returnGeneSetsAsList

Return pre-defined gene lists in several biomedical categories.

Red and Green Color Specification

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

Put close eigenvectors next to each other

Apply a function to each set in a multiData structure.

Number of present data entries.

Green-black-red color sequence

Pad numbers with leading zeros to specified total width

proportionsInAdmixture

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

Calculate overlap of modules

simulateDatExpr5Modules

Simplified simulation of expression data

plotClusterTreeSamples

Annotated clustering dendrogram of microarray samples

Prepend a comma to a non-empty string

stratifiedBarplot

Bar plots of data across two splitting parameters

simulateEigengeneNetwork

Simulate eigengene network from a causal model

goodSamplesGenesMS

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

signumAdjacencyFunction

Hard-thresholding adjacency function

Topological overlap for a subset of the whole set of genes

moduleColor.getMEprefix

Get the prefix used to label module eigengenes.

Union and intersection of multiple sets

Fast calculations of Pearson correlation.

modulePreservation

Calculation of module preservation statistics

intramodularConnectivity

Calculation of intramodular connectivity

vectorizeMatrix

Turn a matrix into a vector of non-redundant components

propVarExplained

Proportion of variance explained by eigengenes.

plotDendroAndColors

Dendrogram plot with color annotation of objects

Simulate a gene co-expression module

Barplot with text or color labels.

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

Visual check of scale-free topology

scaleFreeFitIndex

Calculation of fitting statistics for evaluating scale free topology fit.

transposeBigData

Transpose a big matrix or data frame

verboseScatterplot

Scatterplot annotated by regression line and p-value

sigmoidAdjacencyFunction

Sigmoid-type adacency function.

Scatterplot with density

lowerTri2matrix

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

Red and Green Color Image of Data Matrix

plotColorUnderTree

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

normalizeLabels

Transform numerical labels into normal order.

recutConsensusTrees

Repeat blockwise consensus module detection from pre-calculated data

relativeCorPredictionSuccess

Compare prediction success

recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

Signed eigengene-based connectivity

standardScreeningNumericTrait

Standard screening for numeric traits

Space-less paste

Pairwise scatterplots of eigengenes

mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

networkScreeningGS

Network gene screening with an external gene significance measure

projectiveKMeans

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

qvalue.restricted

qvalue convenience wrapper

Opens a graphics window with specified dimensions

Boxplot annotated by a Kruskal-Wallis p-value

swapTwoBranches

Select, swap, or reflect branches in a dendrogram.

userListEnrichment

Measure enrichment between inputted and user-defined lists

unsignedAdjacency

Calculation of unsigned adjacency