Hierarchical consensus calculation of module eigengene dissimilarities, or more generally, correlation-based dissimilarities of sets of vectors.
hierarchicalConsensusMEDissimilarity(
MEs,
networkOptions,
consensusTree,
greyName = "ME0",
calibrate = FALSE)A multiData structure containing vectors (usually module eigengenes) whose consensus
dissimilarity is to be calculated.
A multiData structure containing, for each input data set, a list of class NetworkOptions giving options for network calculation for all of the networks.
A list specifying the consensus calculation. See details.
Name of the "grey" module eigengene. Currently not used.
Logical: should the dissimilarities be calibrated using the calibration method specified in
consensusTree? See details.
A matrix with rows and columns corresponding to the variables (modules) in MEs, containing the consensus dissimilarities.
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.
hierarchicalConsensusCalculation for the actual consensus calculation.