WGCNA

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)

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

Graphical representation of the Topological Overlap Matrix

Topological overlap matrix similarity and dissimilarity

Topological overlap matrix

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 error bars to a barplot.

Add grid lines to an existing plot.

Add vertical “guide lines” to a dendrogram plot

Add trait information to multi-set module eigengene structure

Calculate network adjacency

Adjacency matrix based on polynomial regression

Calculate network adjacency based on natural cubic spline regression

Align expression data with given vector

Divide tasks among workers

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

Biweight Midcorrelation

Calculation of biweight midcorrelations and associated p-values

Weights used in biweight midcovariance

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

Blue-white-red color sequence

Branch dissimilarity based on eigennodes (eigengenes).

Branch split.

Branch split based on dissimilarity.

Branch split (dissimilarity) statistics derived from labels determined from a stability study

Check adjacency matrix

Check structure and retrieve sizes of a group of datasets.

Chooses a single hub gene in each module

Chooses the top hub gene in each module

Clustering coefficient calculation

Co-clustering measure of cluster preservation between two clusterings

Permutation test for co-clustering

Fast colunm- and row-wise quantile of a matrix.

Select one representative row per group

Selects one representative row per group based on kME

Iterative garbage collection.

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

Consensus network (topological overlap).

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

Preservation of eigengene correlations

Deviance- and martingale residuals from a Cox regression model

Constant-height tree cut

Constant height tree cut using color labels

Show colors used to label modules

Threshold for module merging

Empirical Bayes-moderated adjustment for unwanted covariates

Export network to Cytoscape

Export network data in format readable by VisANT

Turn non-numeric columns into factors

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

Green-black-red color sequence

Green-white-red color sequence

Hierarchical consensus calculation

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

Hubgene significance

Impute missing data separately in each module

Calculate individual correlation network matrices

Inline display of progress

Calculation of intramodular connectivity

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

Label scatterplot points

Barplot with text or color labels.

Produce a labeled heatmap plot

Labeled heatmap divided into several separate plots.

Convert numerical labels to colors.

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

Construct a network from a matrix

Merge close modules in gene expression data

Meta-analysis of binary and continuous variables

Meta-analysis Z statistic

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.

Merge modules and reassign genes using kME.

Fixed-height cut of a dendrogram.

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

Create a multiData structure.

Eigengene significance across multiple sets

Analogs of grep(l) and (g)sub for multiple patterns and relacements

Calculate module eigengenes.

Union and intersection of multiple sets

Calculate weighted adjacency matrices based on mutual information

Number of present data entries.

Number of sets in a multi-set variable

Nearest centroid predictor

Connectivity to a constant number of nearest neighbors

Connectivity to a constant number of nearest neighbors across multiple data sets

Calculations of network concepts

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 new consensus tree

Creates a list of correlation 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

Calculate overlap of modules

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

Eigengene network plot

Pairwise scatterplots of eigengenes

Red and Green Color Image of Data Matrix

Barplot of module significance

Plot multiple histograms in a single plot

Network heatmap plot

Estimate the population-specific mean values in an admixed population.

Parallel quantile, median, mean

Prepend a comma to a non-empty string

Pad numbers with leading zeros to specified total width

Network preservation calculations

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

qvalue convenience wrapper

Rand index of two partitions

Estimate the p-value for ranking consistently high (or low) on multiple lists

Repeat blockwise module detection from pre-calculated data