WGCNA: branchEigengeneDissim – R documentation

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branchEigengeneDissim

Branch dissimilarity based on eigennodes (eigengenes).

Description

Calculation of branch dissimilarity based on eigennodes (eigengenes) in single set and multi-data situations. This function is used as a plugin for the dynamicTreeCut package and the user should not call this function directly. This function is experimental and subject to change.

Usage

branchEigengeneDissim(
  expr, 
  branch1, branch2, 
  corFnc = cor, corOptions = list(use = "p"), 
  signed = TRUE, ...)

branchEigengeneSimilarity(
  expr, 
  branch1, 
  branch2, 
  networkOptions, 
  returnDissim = TRUE, ...)

mtd.branchEigengeneDissim(
  multiExpr, 
  branch1, branch2,
  corFnc = cor, corOptions = list(use = 'p'),
  consensusQuantile = 0, 
  signed = TRUE, reproduceQuantileError = FALSE, ...)

hierarchicalBranchEigengeneDissim(
    multiExpr,
    branch1, branch2,
    networkOptions,
    consensusTree, ...)

Arguments

`expr`	Expression data.
`multiExpr`	Expression data in multi-set format.
`branch1`	Branch 1.
`branch2`	Branch 2.
`corFnc`	Correlation function.
`corOptions`	Other arguments to the correlation function.
`consensusQuantile`	Consensus quantile.
`signed`	Should the network be considered signed?
`reproduceQuantileError`	Logical: should an error in the calculation from previous versions, which caused the true consensus quantile to be `1-consensusQuantile` rather than `consensusQuantile`, be reproduced? Use this only to reproduce old calculations.
`networkOptions`	An object of class `NetworkOptions` giving the network construction options to be used in the calculation of the similarity.
`returnDissim`	Logical: if `TRUE`, dissimarity, rather than similarity, will be returned.
`consensusTree`	A list of class `ConsensusTree` specifying the consensus calculation. Note that calibration options within the consensus specifications are ignored: since the consensus is calulated from entries representing a single value, calibration would not make sense.
`...`	Other arguments for compatibility; currently unused.

Details

These functions calculate the similarity or dissimilarity of two groups of genes (variables) in expr or multiExpr using correlations of the first singular vectors ("eigengenes"). For a single data set (branchEigengeneDissim and branchEigengeneSimilarity), the similarity is the correlation, and dissimilarity 1-correlation of the first signular vectors.

Functions mtd.branchEigengeneDissim and hierarchicalBranchEigengeneDissim calculate consensus eigengene dissimilarity. Function mtd.branchEigengeneDissim calculates a simple ("flat") consensus of branch eigengene similarities across the given data set, at the given consensus quantile. Function hierarchicalBranchEigengeneDissim can calculate a hierarchical consensus in which consensus calculations are hierarchically nested.

Value

A single number, the dissimilarity for branchEigengeneDissim, mtd.branchEigengeneDissim, and hierarchicalBranchEigengeneDissim.

branchEigengeneSimilarity returns similarity or dissimilarity, depending on imput.

Author(s)

Peter Langfelder