aracne {MetNet} | R Documentation

## Create an adjacency matrix based on algorithm for the reconstruction of accurate cellular networks

### Description

'aracne' infers an adjacency matrix using the algorithm for the reconstruction of accurate cellular networks using the 'aracne.a' function from the 'parmigene' package. The function 'aracne' will return the weighted adjacency matrix of the inferred network after applying 'aracne.a'.

### Usage

    aracne(mi, eps = 0.05)

### Arguments

`mi` | matrix, where columns and the rows are features (metabolites), cell entries are mutual information values between the features. As input, the mutual information (e.g. raw MI estimates or Jackknife bias corrected MI estimates) from the 'cmi' function of the 'mpmi' package can be used.
`eps` | numeric, used to remove the weakest edge of each triple of nodes

### Details

For more details on the 'aracne.a' function, refer to '?parmigene::aracne.a'. 'aracne.a' considers each triple of edges independently and removes the weakest one if MI(i, j) < MI(j, k) - eps and MI(i, j) < MI(i, k) - eps. See Margolin et al. (2006) for further information.

### Value

matrix, matrix with edges inferred from Reconstruction of accurate cellular networks algorithm 'aracne.a'

### Author(s)

Thomas Naake, thomasnaake@googlemail.com

### References

Margolin et al. (2006): ARACNE : An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, S7, doi: [10.1186/1471-2105-7-S1-S7](https://doi.org/10.1186/1471-2105-7-S1-S7)

### Examples

    data("x_test", package = "MetNet")
    x <- x_test[1:10, 3:ncol(x_test)]
    x <- as.matrix(x)
    x_z <- t(apply(x, 1, function(y) (y - mean(y)) / sd(y)))
    mi_x_z <- mpmi::cmi(x_z)$bcmi
    aracne(mi_x_z, eps = 0.05)

[Package MetNet version 1.4.2 Index]