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.
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, ...)
Expression data.
Expression data in multi-set format.
Branch 1.
Branch 2.
Correlation function.
Other arguments to the correlation function.
Consensus quantile.
Should the network be considered signed?
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.
An object of class NetworkOptions
giving the network construction
options to be used in the calculation of the similarity.
Logical: if TRUE
, dissimarity, rather than similarity, will be returned.
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.
A single number, the dissimilarity for branchEigengeneDissim
, mtd.branchEigengeneDissim
, and
hierarchicalBranchEigengeneDissim
.
branchEigengeneSimilarity
returns similarity or dissimilarity, depending on imput.
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.