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