Reverse-engineer the network
Reverse-engineer the network.
## 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"
)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"'. |
A network object.
Nicolas Jung, Frédéric Bertrand , Myriam Maumy-Bertrand.
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.
#With simulated data data(M) infM <- inference(M) str(infM) #With selection of genes from GSE39411 data(Selection) infSel <- inference(Selection) str(infSel)
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