branchEigengeneDissim: Branch dissimilarity based on eigennodes (eigengenes).

View source: R/branchSplit.R

branchEigengeneDissimR Documentation

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 April 23, 2022, 1:06 a.m.