aracne | R Documentation |

'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'.

aracne(mi, eps = 0.05, ...)

`mi` |
matrix, where columns are the samples 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) from the 'knnmi.all' function of the 'parmigene' package can be used. |

`eps` |
numeric, used to remove the weakest edge of each triple of nodes |

`...` |
not used here |

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.

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

Thomas Naake, thomasnaake@googlemail.com

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)

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

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