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