aracne {minet} | R Documentation

## Algorithm for the Reconstruction of Accurate Cellular NEtworks

### Description

This function takes the mutual information matrix as input in order to return the infered network according to the Aracne algorithm. This algorithm applies the data processing inequality to all triplets of nodes in order to remove the least significant edge in each triplet.

### Usage

    aracne( mim, eps=0 )

### Arguments

`mim` | A square matrix whose i,j th element is the mutual information between variables X_i and X_j - see `build.mim`.
`eps` | Numeric value indicating the threshold used when removing an edge : for each triplet of nodes (i,j,k), the weakest edge, say (ij), is removed if its weight is below min{(ik),(jk)}-eps - see references.

### Details

The Aracne procedure starts by assigning to each pair of nodes a weight equal to their mutual information. Then, the weakest edge of each triplet is interpreted as an indirect interaction and is removed if the difference between the two lowest weights is above a threshold `eps`.

### Value

`aracne` returns a matrix which is the weighted adjacency matrix of the network. In order to display the network, load the package Rgraphviz and use the following command:
plot( as( returned.matrix ,"graphNEL") )

### References

Adam A. Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera, and Andrea Califano. Aracne : An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 2006.

### See Also

`build.mim`, `clr`, `mrnet`

### Examples

    data(syn.data)
    mim <- build.mim(discretize(syn.data))
    net <- aracne(mim)

[Package minet version 1.4.0 Index]