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]