mRMRe.Network-class: Class '"mRMRe.Network"'

In bhklab/mRMRe: Parallelized Minimum Redundancy, Maximum Relevance (mRMR)

Description

mRMRe.Network is a wrapper for inferring a network of features based on mRMR feature selection.

Instantiation

Objects are created via calls of the form new("mRMRe.Network", data, prior_weight, target_indices, levels, layers, ..., mi_threshold, causality_threshold).

layers: is expected to be an integer specifying the number of layers of network inference desired. When multiple layers are desired, the elements of the solutions found in the last step of feature selection are used as the targets of the next step.

Since networking involves filter processing, the remaining arguments are identical to those required by solutions method of the mRMRe.Filter object and mim method of the mRMRe.Data object.

Slots

topologies:

Object of class "list" ~~

mi_matrix:

Object of class "matrix" containing the combined mutual information matrix of the network elements.

causality_list:

Object of class "list" containing for each target a vector of causality coefficients between the target and its predictors.

sample_names:

Object of class "character" containing the sample names.

feature_names:

Object of class "character" containing the feature names.

target_indices:

Object of class "integer" containing the target indices.

Methods

adjacencyMatrix

signature(object = "mRMRe.Network"): Returns a matrix describing the topology of the network.

adjacencyMatrixSum

signature(object = "mRMRe.Network"): ...

causality

signature(object = "mRMRe.Network"): Returns a list containing vectors containing causality coefficients between targets and predictors.

featureNames

signature(object = "mRMRe.Network"): Returns a vector containing the feature names.

mim

signature(object = "mRMRe.Network"): ...

sampleNames

signature(object = "mRMRe.Network"): Returns a vector containing sample names.

solutions

signature(object = "mRMRe.Network"): ...

visualize

signature(object = "mRMRe.Network"): ...

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

See Also

mRMRe.Filter-class, mRMRe.Data-class

Examples

showClass("mRMRe.Network")

set.thread.count(2)

## load data
data(cgps)

## build an mRMRe.Data object
ge <- mRMR.data(data = data.frame(cgps.ge[ , 1:100, drop=FALSE]))

## build a network object with the 10 first genes and their children,
## 8 distinct mRMR feature selections of 5 genes for each gene
exect <- system.time(netw <- new("mRMRe.Network", data = ge, target_indices = 1:10,
		levels = c(8, 1, 1, 1, 1), layers = 2))
print(exect)

## plot network using igraph
## Not run: visualize(netw)

bhklab/mRMRe documentation built on Sept. 3, 2021, 10:50 p.m.