branchEigengeneDissim | R Documentation |

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, ...)

`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 |

`networkOptions` |
An object of class |

`returnDissim` |
Logical: if |

`consensusTree` |
A list of class |

`...` |
Other arguments for compatibility; currently unused. |

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.

A single number, the dissimilarity for `branchEigengeneDissim`

, `mtd.branchEigengeneDissim`

, and
`hierarchicalBranchEigengeneDissim`

.

`branchEigengeneSimilarity`

returns similarity or dissimilarity, depending on imput.

Peter Langfelder

`hierarchicalConsensusCalculation`

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