aracne {MetNet} | R Documentation |
.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.
aracne(mi, eps=0.05, aracne_threshold=0)
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 |
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. |
For more details on the aracne.a
function,
refer to ?parmigene::aracne.a.
matrix, matrix with edges inferred from Reconstruction of accurate
cellular networks algorithm aracne
Thomas Naake, thomasnaake @googlemail.com
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)