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