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