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