SelectBoost (version 2.2.2)

selectboost_cascade: Selectboost_cascade

Description

Selectboost for Cascade inference.

Usage

selectboost(M, ...)
# S4 method for micro_array
selectboost(
  M,
  Fabhat,
  K = 5,
  eps = 10^-5,
  cv.subjects = TRUE,
  ncores = 4,
  use.parallel = FALSE,
  verbose = FALSE,
  group = group_func_2,
  c0value = 0.95
)

Value

A network.confidence object.

Arguments

M

Microarray class from the Cascade package.

...

Additionnal arguments. Not used.

Fabhat

F matrix inferred using the inference function from the Cascade package.

K

Number of crossvalidation folds.

eps

Threshold for assinging a zero value to an inferred parameter. Defaults to 10^-5.

cv.subjects

Crossvalidation is made subjectwise using leave one out. Discards the K option.

ncores

Numerical value. Number of cores for parallel computing. Defaults to 4.

use.parallel

Boolean. To use parallel computing (doMC) download the extended package from Github. Set to FALSE.

verbose

Boolean. Defaults to FALSE.

group

Function. The grouping function. Defaults to group_func_2.

c0value

Numeric. c0 value to use for confidence computation. Defaults to TRUE

Author

Frederic Bertrand,

Details

Extending results from the Cascade package: providing confidence indices for the reverse engineered links.

Reference for the Cascade modelling Vallat, L., Kemper, C. a., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., Pocheville, A., Fisher, J. W., Gribben, J. G. et 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 of the United States of America, 110(2), 459-64.

Reference for the Cascade package Jung, N., Bertrand, F., Bahram, S., Vallat, L. et Maumy-Bertrand, M. (2014). Cascade : A R package to study, predict and simulate the diffusion of a signal through a temporal gene network. Bioinformatics. ISSN 13674803..

References

selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. tools:::Rd_expr_doi("10.1093/bioinformatics/btaa855")

See Also

boost, fastboost, plot.selectboost, inference

Other Selectboost functions: autoboost(), boost, fastboost(), plot_selectboost_cascade

Examples

set.seed(314)
set.seed(314)

# \donttest{
data(Cascade_example)
Fab_inf_C <- Net_inf_C@F
#By default community grouping of variables
set.seed(1)
net_confidence <- selectboost(M, Fab_inf_C)
net_confidence_.5 <- selectboost(M, Fab_inf_C, c0value = .5)
#With group_func_1, variables are grouped by thresholding the correlation matrix
net_confidence_thr <- selectboost(M, Fab_inf_C, group = group_func_1)
# }