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