Generalized Topological Overlap Measure
Topological overlap matrix
Align expression data with given vector
Divide tasks among workers
Estimate the true trait underlying a list of surrogate markers.
Calculation of block-wise topological overlaps
Automatic network construction and module detection
branchSplitFromStabilityLabels
Branch split (dissimilarity) statistics derived from labels determined from a stability study
Check adjacency matrix
Put close eigenvectors next to each other in several sets.
consensusProjectiveKMeans
Consensus projective K-means (pre-)clustering of expression data
Get all elementary inputs in a consensus tree
convertNumericColumnsToNumeric
Convert character columns that represent numbers to numeric
factorizeNonNumericColumns
Turn non-numeric columns into factors
Put single-set data into a form useful for multiset calculations.
Break long character strings into multiple lines
fundamentalNetworkConcepts
Calculation of fundamental network concepts from an adjacency matrix.
Pathways with Corresponding Gene Markers - Compiled by Mike Palazzolo and Jim Wang from CHDI
hierarchicalConsensusCalculation
Hierarchical consensus calculation
Stem Cell-Related Genes with Corresponding Gene Markers
Calculation of measures of fuzzy module membership (KME) in hierarchical consensus modules
Label scatterplot points
Graphical representation of the Topological Overlap Matrix
Barplot with text or color labels.
Topological overlap matrix similarity and dissimilarity
Meta-analysis of binary and continuous variables
Meta-analysis Z statistic
Calculation of biweight midcorrelations and associated p-values
Weights used in biweight midcovariance
binarizeCategoricalColumns
Turn categorical columns into sets of binary indicators
binarizeCategoricalVariable
Turn a categorical variable into a set of binary indicators
Turn a multiData structure into a single matrix or data frame.
Chooses the top hub gene in each module
Clustering coefficient calculation
Selects one representative row per group based on kME
Iterative garbage collection.
Set attributes on each component of a multiData structure
Calculate module eigengenes.
Consensus selection of group representatives
Consensus network (topological overlap).
Union and intersection of multiple sets
Export network to Cytoscape
Gene Markers for Regions of the Human Brain
Export network data in format readable by VisANT
Create a list of network construction arguments (options).
Green-black-red color sequence
Green-white-red color sequence
Transform numerical labels into normal order.
Blood Cell Types with Corresponding Gene Markers
Hubgene significance
Impute missing data separately in each module
Convert numerical labels to colors.
Analysis of scale free topology for hard-thresholding.
Accuracy measures for a 2x2 confusion matrix or for vectors of predicted and observed values.
Calculation of GO enrichment (experimental)
Brain-Related Categories with Corresponding Gene Markers
Add error bars to a barplot.
Allow and disable multi-threading for certain WGCNA calculations
Analysis of scale free topology for soft-thresholding
Red and Green Color Image of Data Matrix
Projective K-means (pre-)clustering of expression data
Barplot of module significance
Convert a list to a multiData structure and vice-versa.
preservationNetworkConnectivity
Network preservation calculations
Fast joint calculation of row- or column-wise minima and indices of minimum elements
Add trait information to multi-set module eigengene structure
Calculate network adjacency
Adjacency matrix based on polynomial regression
automaticNetworkScreening
One-step automatic network gene screening
Prediction of Weighted Mutual Information Adjacency Matrix by Correlation
Fixed-height cut of a dendrogram.
Get the prefix used to label module eigengenes.
Calculation of module preservation statistics
Branch split.
Branch split based on dissimilarity.
Check structure and retrieve sizes of a group of datasets.
Chooses a single hub gene in each module
Red-white-green color sequence
conformityBasedNetworkConcepts
Calculation of conformity-based network concepts.
Attempt to calculate an appropriate block size to maximize efficiency of block-wise calcualtions.
Calculate network adjacency based on natural cubic spline regression
Conformity and module based decomposition of a network adjacency matrix.
Student asymptotic p-value for correlation
Various basic operations on BlockwiseData
objects.
Preservation of eigengene correlations
Add grid lines to an existing plot.
Add vertical ``guide lines'' to a dendrogram plot
automaticNetworkScreeningGS
One-step automatic network gene screening with external gene significance
Deviance- and martingale residuals from a Cox regression model
Constant-height tree cut
blockwiseConsensusModules
Find consensus modules across several datasets.
relativeCorPredictionSuccess
Compare prediction success
Filter samples with too many missing entries
Co-clustering measure of cluster preservation between two clusterings
Iterative filtering of samples and genes with too many missing entries
Calculate individual correlation network matrices
Number of sets in a multi-set variable
Nearest centroid predictor
Create a new consensus tree
Creates a list of correlation options.
Biweight Midcorrelation
Visual check of scale-free topology
Blue-white-red color sequence
selectFewestConsensusMissing
Select columns with the lowest consensus number of missing data
coClustering.permutationTest
Permutation test for co-clustering
Color representation for a numeric variable
Fast colunm- and row-wise quantile of a matrix.
Inline display of progress
Inline display of progress
Branch dissimilarity based on eigennodes (eigengenes).
Select one representative row per group
Calculation of intramodular connectivity
Calculate consensus kME (eigengene-based connectivities) across multiple data sets.
Calculation of a (single) consenus with optional data calibration.
Consensus dissimilarity of module eigengenes.
Qunatification of success of gene screening
orderBranchesUsingHubGenes
Optimize dendrogram using branch swaps and reflections.
Eigengene network plot
Consensus clustering based on topological overlap and hierarchical clustering
Determine whether the supplied object is a valid multiData structure
Fisher's asymptotic p-value for correlation
Reconstruct a symmetric matrix from a distance (lower-triangular) representation
Fast calculations of Pearson correlation.
Pairwise scatterplots of eigengenes
Relabel module labels to best match the given reference labels
Proportion of variance explained by eigengenes.
Calculation of correlations and associated p-values
Threshold for module merging
Apply a function to each set in a multiData structure.
Estimate the proportion of pure populations in an admixed population based on marker expression
values.
Apply a function to elements of given multiData structures.
nearestNeighborConnectivity
Connectivity to a constant number of nearest neighbors
Constant height tree cut using color labels
Show colors used to label modules
Repeat blockwise module detection from pre-calculated data
Filter genes with too many missing entries
Empirical Bayes-moderated adjustment for unwanted covariates
Iterative filtering of samples and genes with too many missing entries across multiple data sets
Filter genes with too many missing entries across multiple sets
Simulate eigengene network from a causal model
Filter samples with too many missing entries across multiple data sets
nearestNeighborConnectivityMS
Connectivity to a constant number of nearest neighbors across multiple data sets
Calculation of hierarchical consensus topological overlap matrix
Network gene screening with an external gene significance measure
Simulate a gene co-expression module
Create a list holding information about dividing data into blocks
Repeat blockwise consensus module detection from pre-calculated data
hierarchicalMergeCloseModules
Merge close (similar) hierarchical consensus modules
Produce a labeled heatmap plot
hierarchicalConsensusMEDissimilarity
Hierarchical consensus calculation of module eigengene dissimilarity
Calculate overlap of modules
Determines significant overlap between modules in two networks based on kME tables.
Function to plot kME values between two comparable data sets.
hierarchicalConsensusModules
Hierarchical consensus network construction and module identification
Keep probes that are shared among given data sets
Standard error of the mean of a given vector.
Labeled heatmap divided into several separate plots.
Sigmoid-type adacency function.
Calculate module eigengenes.
Signed eigengene-based connectivity
Construct a network from a matrix
Merge modules and reassign genes using kME.
Bar plots of data across two splitting parameters
Measure enrichment between inputted and user-defined lists
Merge close modules in gene expression data
Get and set column names in a multiData structure.
populationMeansInAdmixture
Estimate the population-specific mean values in an admixed population.
Topological overlap for a subset of the whole set of genes
If possible, simplify a multiData structure to a 3-dimensional array.
Parallel quantile, median, mean
multiData.eigengeneSignificance
Eigengene significance across multiple sets
Simulation of expression data
Subset rows and columns in a multiData structure
Create a multiData structure.
Analogs of grep(l) and (g)sub for multiple patterns and relacements
Calculations of network concepts
Simplified simulation of expression data
Calculate weighted adjacency matrices based on mutual information
Number of present data entries.
Create, merge and expand BlockwiseData objects
Opens a graphics window with specified dimensions
Create a list holding consensus calculation options.
Put close eigenvectors next to each other
Estimate the q-values for a given set of p-values
orderMEsByHierarchicalConsensus
Order module eigengenes by their hierarchical consensus similarity
qvalue convenience wrapper
Identification of genes related to a trait
Annotated clustering dendrogram of microarray samples
Plot color rows in a given order, for example under a dendrogram
Red and Green Color Image of Correlation Matrix
Dendrogram plot with color annotation of objects
Plot multiple histograms in a single plot
Replace missing values with a constant.
Return pre-defined gene lists in several biomedical categories.
Network heatmap plot
pruneAndMergeConsensusModules
Iterative pruning and merging of (hierarchical) consensus modules
sizeRestrictedClusterMerge
Cluter merging with size restrictions
Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity
Transpose a big matrix or data frame
Calculation of unsigned adjacency
Prepend a comma to a non-empty string
Red and Green Color Specification
Removes the grey eigengene from a given collection of eigengenes.
removePrincipalComponents
Remove leading principal components from data
Blockwise module identification in sampled data
sampledHierarchicalConsensusModules
Hierarchical consensus module identification in sampled data
Pad numbers with leading zeros to specified total width
simpleConsensusCalculation
Simple calculation of a single consenus
simpleHierarchicalConsensusCalculation
Simple hierarchical consensus calculation
Calculation of fitting statistics for evaluating scale free topology fit.
Calculates connectivity of a weighted network.
Simulate multi-set expression data
Simulate small modules
Space-less paste
Boxplot annotated by a Kruskal-Wallis p-value
Colors this library uses for labeling modules.
Rand index of two partitions
Estimate the p-value for ranking consistently high (or low) on multiple lists
Scatterplot with density
standardScreeningBinaryTrait
Standard screening for binatry traits
Topological overlap for a subset of a whole set of genes
Select, swap, or reflect branches in a dendrogram.
Scatterplot annotated by regression line and p-value
setCorrelationPreservation
Summary correlation preservation measure
Voting linear predictor
Shorten given character strings by truncating at a suitable separator.
Round numeric columns to given significant digits.
Hard-thresholding adjacency function
standardScreeningCensoredTime
Standard Screening with regard to a Censored Time Variable
standardScreeningNumericTrait
Standard screening for numeric traits
Turn a matrix into a vector of non-redundant components
Barplot with error bars, annotated by Kruskal-Wallis or ANOVA p-value
Immune Pathways with Corresponding Gene Markers