WGCNA: hierarchicalConsensusMEDissimilarity – R documentation

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hierarchicalConsensusMEDissimilarity

Hierarchical consensus calculation of module eigengene dissimilarity

Description

Hierarchical consensus calculation of module eigengene dissimilarities, or more generally, correlation-based dissimilarities of sets of vectors.

Usage

hierarchicalConsensusMEDissimilarity(
   MEs, 
   networkOptions, 
   consensusTree, 
   greyName = "ME0", 
   calibrate = FALSE)

Arguments

`MEs`	A `multiData` structure containing vectors (usually module eigengenes) whose consensus dissimilarity is to be calculated.
`networkOptions`	A `multiData` structure containing, for each input data set, a list of class `NetworkOptions` giving options for network calculation for all of the networks.
`consensusTree`	A list specifying the consensus calculation. See details.
`greyName`	Name of the "grey" module eigengene. Currently not used.
`calibrate`	Logical: should the dissimilarities be calibrated using the calibration method specified in `consensusTree`? See details.

Details

This function first calculates the similarities of the ME vectors from their correlations, using the appropriate options in networkOptions (correlation type and options, signed or unsigned dissimilarity etc). This results in a similarity matrix in each of the input data sets.

Next, a hierarchical consensus of the similarities is calculated via a call to hierarchicalConsensusCalculation, using the consensus specification and options in consensusTree. In typical use, consensusTree contains the same consensus specification as the consensus network calculation that gave rise to the consensus modules whose eigengenes are contained in MEs but this is not mandatory.

The argument consensusTree should have the following components: (1) inputs must be either a character vector whose components match names(inputData), or consensus trees in the own right. (2) consensusOptions must be a list of class "ConsensusOptions" that specifies options for calculating the consensus. A suitable set of options can be obtained by calling newConsensusOptions. (3) Optionally, the component analysisName can be a single character string giving the name for the analysis. When intermediate results are returned, they are returned in a list whose names will be set from analysisName components, if they exist.

In the final step, the consensus similarity is turned into a dissimilarity by subtracting it from 1.

Value

A matrix with rows and columns corresponding to the variables (modules) in MEs, containing the consensus dissimilarities.

Author(s)

Peter Langfelder