branchEigengeneDissim: Branch dissimilarity based on eigennodes (eigengenes).

In WGCNA: Weighted Correlation Network Analysis

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

See Also

hierarchicalConsensusCalculation

WGCNA documentation built on Sept. 18, 2024, 5:08 p.m.