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