Cascade (version 1.7)

inference: Reverse-engineer the network

Description

Reverse-engineer the network.

Usage

inference(M,…)

Arguments

M

a micro_array object.

…

Optional arguments:

tour.max=30

maximal number of steps.

g=function(x) 1/x

the new solution is choosen as (the old solution + g(x) * the new solution)/(1+g(x)) where x is the number of steps.

conv=10e-3

convergence criterion.

cv.subjects=TRUE

should the cross validation be done removing the subject one by one ?

nb.folds=NULL

Relevant only if cv.subjects is FALSE. The number of folds in cross validation.

eps=10e-5

machine zero

type.inf="iterative"

"iterative" or "noniterative" : should the algorithm be computed iteratively

Value

A network object.

References

Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014). Cascade: a R-package to study, predict and simulate the diffusion of a signal through a temporal gene network. Bioinformatics, btt705.

Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram, S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.

Examples

# NOT RUN {
#With simulated data
data(simul)
infM <- inference(M)
str(infM)

#With selection of genes from GSE39411
data(Selection)
infSel <- inference(Selection)
str(infSel)
# }