aracne {MetNet} | R Documentation

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

### Description

`.information` infers an adjacency matrix using the algorithm for the reconstruction of accurate cellular networks using the `aracne.a` function from the `parmigene` package. The presence/absence is based on if the returned value exceeds a user-defined threshold value. `aracne` will return the adjacency matrix containing the presence/absence value.

### Usage

    aracne(mi, eps=0.05, aracne_threshold=0)

### 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
`aracne_threshold` | numeric, if the aracne value exceeds the threshold (aracne$_i,j$ > threshold, where aracne$_i, j$ is the aracne value of the ith row feature and of the jth column feature), the connection is defined as present, if the aracne value is lower than the threshold value (aracne$_i,j$ <= threshold) there is no statistical connection reported.

### Details

For more details on the `aracne.a` function, refer to ?parmigene::aracne.a.

### Value

matrix, matrix with edges inferred from Reconstruction of accurate cellular networks algorithm `aracne`

### Author(s)

Thomas Naake, thomasnaake @googlemail.com

### Examples

    data("x_test", package="MetNet")
    x <- x_test[, 3:dim(x_test)[2]]
    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, aracne_threshold=0)

[Package MetNet version 1.2.0 Index]