#Introduction
Extending results from the Cascade package: reverse engineering with selectboost to compute confidence indices for a fitted model. We first fit a model to a cascade network using the Cascade package inference function then we compute confidence indices for the inferred links using the Selecboost algorithm.
If you are a Linux/Unix or a Macos user, you can install a version of SelectBoost with support for doMC from github with:
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.
Code to reproduce the datasets saved with the package and some the figures of the article Aouadi et al. (2018), <arXiv:1810.01670>
We define the F array for the simulations.
T<-4
F<-array(0,c(T-1,T-1,T*(T-1)/2))
for(i in 1:(T*(T-1)/2)){diag(F[,,i])<-1}
F[,,2]<-F[,,2]*0.2
F[2,1,2]<-1
F[3,2,2]<-1
F[,,4]<-F[,,2]*0.3
F[3,1,4]<-1
F[,,5]<-F[,,2]We set the seed to make the results reproducible
set.seed(1)
Net<-Cascade::network_random(
nb=100,
time_label=rep(1:4,each=25),
exp=1,
init=1,
regul=round(rexp(100,1))+1,
min_expr=0.1,
max_expr=2,
casc.level=0.4
)
Net@F<-FWe simulate gene expression according to the network Net
We infer the new network.
Heatmap of the coefficients of the Omega matrix of the network. Run the code to get the graph.
By default the crossvalidation is made subjectwise according to a leave one out scheme and the resampling analysis is made at the .95 c0 level. To pass CRAN tests, use.parallel = FALSE is required. Set use.parallel = TRUE and select the number of cores using ncores = 4.
Use cv.subject=FALSE to use default crossvalidation
Use plot to display the result of the confidence analysis.
Run the code to plot the other results.
Run the code to plot the remaning graphs.
Distribution of non-zero (absolute value > 1e-5) coefficients
Plot of confidence at .95 resampling level versus coefficient value for non-zero (absolute value > 1e-5) coefficients
Plot of confidence at .5 resampling level versus coefficient value for non-zero (absolute value > 1e-5) coefficients
Plot of confidence at .95 resamling level with groups created by thresholding the correlation matrix versus coefficient value for non-zero (absolute value > 1e-5) coefficients.
Plot of confidence at .95 resampling level versus coefficient value for non-zero (absolute value > 1e-5) coefficients using standard cross-validation.
Either decrease percent selected or choose c0 as a quantile.