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, ...)

Value

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

branchEigengeneSimilarity returns similarity or dissimilarity, depending on imput.

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.

Author

Peter Langfelder

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.