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