inference,micro_array-method {Cascade}

Reverse-engineer the network

Description

Reverse-engineer the network.

Usage

## S4 method for signature 'micro_array'
inference(
  M,
  tour.max = 30,
  g = function(x) {
     1/x
 },
  conv = 0.001,
  cv.subjects = TRUE,
  nb.folds = NULL,
  eps = 10^-5,
  type.inf = "iterative"
)

Arguments

M

a micro_array object.

tour.max

maximal number of steps. Defaults to 'tour.max=30'

g

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

conv

convergence criterion. Defaults to 'conv=10e-3'

cv.subjects

should the cross validation be done removing the subject one by one ? Defaults to 'cv.subjects=TRUE'.

nb.folds

Relevant only if cv.subjects is FALSE. The number of folds in cross validation. Defaults to 'NULL'.

eps

machine zero. Defaults to '10e-5'.

type.inf

"iterative" or "noniterative" : should the algorithm be computed iteratively. Defaults to '"iterative"'.

Value

A network object.

Author(s)

Nicolas Jung, Frédéric Bertrand , Myriam Maumy-Bertrand.

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

#With simulated data
data(M)
infM <- inference(M)
str(infM)

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