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