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
Gene Markers for Regions of the Human Brain
Calculation of GO enrichment (experimental)
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
Graphical representation of the Topological Overlap Matrix
Topological overlap matrix similarity and dissimilarity
Estimate the true trait underlying a list of surrogate markers.
Accuracy measures for a 2x2 confusion matrix or for vectors of predicted and observed values.
Add vertical "guide lines" to a dendrogram plot
Add trait information to multi-set module eigengene structure
Adjacency matrix based on polynomial regression
Calculate network adjacency based on natural cubic spline regression
Allow and disable multi-threading for certain WGCNA calculations
One-step automatic network gene screening
One-step automatic network gene screening with external gene significance
Calculation of biweight midcorrelations and associated p-values
Turn categorical columns into sets of binary indicators
Turn a categorical variable into a set of binary indicators
Attempt to calculate an appropriate block size to maximize efficiency of block-wise calcualtions.
Find consensus modules across several datasets.
Calculation of block-wise topological overlaps
Automatic network construction and module detection
Branch dissimilarity based on eigennodes (eigengenes).
Branch split (dissimilarity) statistics derived from labels determined from a stability study
Check structure and retrieve sizes of a group of datasets.
Chooses a single hub gene in each module
Co-clustering measure of cluster preservation between two clusterings
Fast colunm- and row-wise quantile of a matrix.
Selects one representative row per group based on kME
Calculation of conformity-based network concepts.
Conformity and module based decomposition of a network adjacency matrix.
Calculation of a (single) consenus with optional data calibration.
Consensus clustering based on topological overlap and hierarchical clustering
Calculate consensus kME (eigengene-based connectivities) across multiple data sets.
Consensus dissimilarity of module eigengenes.
Put close eigenvectors next to each other in several sets.
Consensus projective K-means (pre-)clustering of expression data
Consensus selection of group representatives
Get all elementary inputs in a consensus tree
Convert character columns that represent numbers to numeric
Fast calculations of Pearson correlation.
Calculation of correlations and associated p-values
Qunatification of success of gene screening
Fisher's asymptotic p-value for correlation
Student asymptotic p-value for correlation
Deviance- and martingale residuals from a Cox regression model
Constant height tree cut using color labels
Empirical Bayes-moderated adjustment for unwanted covariates
Export network data in format readable by VisANT
Put single-set data into a form useful for multiset calculations.
Break long character strings into multiple lines
Calculation of fundamental network concepts from an adjacency matrix.
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
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
Calculation of measures of fuzzy module membership (KME) in hierarchical consensus modules
Hierarchical consensus calculation of module eigengene dissimilarity
Hierarchical consensus network construction and module identification
Calculation of hierarchical consensus topological overlap matrix
Merge close (similar) hierarchical consensus modules
Impute missing data separately in each module
Calculate individual correlation network matrices
Determine whether the supplied object is a valid multiData structure
Function to plot kME values between two comparable data sets.
Keep probes that are shared among given data sets
Labeled heatmap divided into several separate plots.
Convert a list to a multiData structure and vice-versa.
Reconstruct a symmetric matrix from a distance (lower-triangular) representation
Relabel module labels to best match the given reference labels
Merge close modules in gene expression data
Meta-analysis of binary and continuous variables
Fast joint calculation of row- or column-wise minima and indices of minimum elements
Get the prefix used to label module eigengenes.
Merge modules and reassign genes using kME.
Calculation of module preservation statistics
Apply a function to each set in a multiData structure.
Apply a function to elements of given multiData structures.
Turn a multiData structure into a single matrix or data frame.
Set attributes on each component of a multiData structure
Get and set column names in a multiData structure.
If possible, simplify a multiData structure to a 3-dimensional array.
Subset rows and columns in a multiData structure
Eigengene significance across multiple sets
Analogs of grep(l) and (g)sub for multiple patterns and relacements
Calculate weighted adjacency matrices based on mutual information
Connectivity to a constant number of nearest neighbors
Connectivity to a constant number of nearest neighbors across multiple data sets
Identification of genes related to a trait
Network gene screening with an external gene significance measure
Create a list holding information about dividing data into blocks
Create, merge and expand BlockwiseData objects
Create a list holding consensus calculation options.
Create a list of network construction arguments (options).
Transform numerical labels into normal order.
Color representation for a numeric variable
Optimize dendrogram using branch swaps and reflections.
Put close eigenvectors next to each other
Order module eigengenes by their hierarchical consensus similarity
Determines significant overlap between modules in two networks based on kME tables.
Analysis of scale free topology for hard-thresholding.
Analysis of scale free topology for soft-thresholding
Annotated clustering dendrogram of microarray samples
Plot color rows in a given order, for example under a dendrogram
Red and Green Color Image of Correlation Matrix
Dendrogram plot with color annotation of objects
Red and Green Color Image of Data Matrix
Plot multiple histograms in a single plot
Estimate the population-specific mean values in an admixed population.
Pad numbers with leading zeros to specified total width
Projective K-means (pre-)clustering of expression data
Proportion of variance explained by eigengenes.
Estimate the proportion of pure populations in an admixed population based on marker expression values.
Iterative pruning and merging of (hierarchical) consensus modules
Prune (hierarchical) consensus modules by removing genes with low eigengene-based intramodular connectivity
Estimate the q-values for a given set of p-values
Estimate the p-value for ranking consistently high (or low) on multiple lists
Repeat blockwise module detection from pre-calculated data
Repeat blockwise consensus module detection from pre-calculated data
Removes the grey eigengene from a given collection of eigengenes.
Remove leading principal components from data
Return pre-defined gene lists in several biomedical categories.
Blockwise module identification in sampled data
Hierarchical consensus module identification in sampled data
Calculation of fitting statistics for evaluating scale free topology fit.
Select columns with the lowest consensus number of missing data
Shorten given character strings by truncating at a suitable separator.
Round numeric columns to given significant digits.
Simplified simulation of expression data
Simulate eigengene network from a causal model
Opens a graphics window with specified dimensions
Calculates connectivity of a weighted network.
Colors this library uses for labeling modules.
Standard Screening with regard to a Censored Time Variable
Standard error of the mean of a given vector.
Bar plots of data across two splitting parameters
Topological overlap for a subset of a whole set of genes
Select, swap, or reflect branches in a dendrogram.
Measure enrichment between inputted and user-defined lists
Topological overlap for a subset of the whole set of genes
Turn a matrix into a vector of non-redundant components
Barplot with error bars, annotated by Kruskal-Wallis or ANOVA p-value
Boxplot annotated by a Kruskal-Wallis p-value
Scatterplot annotated by regression line and p-value