regnetMC (regnet with OpenMP support)

Regularized Network-Based Variable Selection

Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al.(2017) and Ren et al.(2019)). Two recent additions are the robust network regularization for the survival response and the network regularization for continuous response. Functions for other regularization methods will be included in the forthcoming upgraded versions.

How to install

• To install the devel version from github, run these two lines of code in R

install.packages("devtools")
devtools::install_github("jrhub/regnetMC")

• Released versions of regnet are available on CRAN.

Examples

Survival response

Example.1 (Robust Network)

data(SurvExample)
X = rgn.surv$X
Y = rgn.surv$Y
clv = c(1:5) # variable 1 to 5 are clinical variables, we choose not to penalize them here.
out = cv.regnet(X, Y, response="survival", penalty="network", clv=clv, robust=TRUE, verbo = TRUE)
out$lambda
fit = regnet(X, Y, "survival", "network", out$lambda[1,1], out$lambda[1,2], clv=clv, robust=TRUE)  
index = which(rgn.surv$beta[-(1:6)] != 0)  # [-(1:6)] removes the intercept and clinical variables that are not subject to selection.
pos = which(fit$coeff[-(1:6)] != 0)  
tp = length(intersect(index, pos))  
fp = length(pos) - tp  
list(tp=tp, fp=fp)  

The cross-validation step can run on multiple cores (OpenMP):

# detect the number of CPU cores on the current host
library("parallel")
ncores = parallel::detectCores(logical=FALSE) # ncores>2 can show significant increases in speed
# parallel CV 
out = cv.regnet(X, Y, response="s", penalty="n", clv=clv, robust=TRUE, ncores=ncores, verbo = TRUE)

Binary response

Example.2 (Network Logistic)

data(LogisticExample)
X = rgn.logi$X
Y = rgn.logi$Y
out = cv.regnet(X, Y, response="binary", penalty="network", folds=5, r = 4.5)  
out$lambda 
fit = regnet(X, Y, "binary", "network", out$lambda[1,1], out$lambda[1,2], r = 4.5)
index = which(rgn.logi$beta[-1] != 0)   # [-1] removes the intercept
pos = which(fit$coeff[-1] != 0)  
tp = length(intersect(index, pos))  
fp = length(pos) - tp  
list(tp=tp, fp=fp)  

Continuous response

Example.3 (Network graphs)

data(ContExample)
X = rgn.tcga$X
Y = rgn.tcga$Y
clv = (1:2)
fit = regnet(X, Y, "continuous", "network", rgn.tcga$lamb1, rgn.tcga$lamb2, clv =clv, alpha.i=0.5)
net = plot(fit)
subs = plot(fit, subnetworks = TRUE, vsize=20, labelDist = 3, theta = 5) 

News

regnetMC (development version) [2021-3]

• regnetMC retains the multiple-cores function of regnet.

regnet (development version) [2020-5]

• cv.regnet() now can run on multiple cores via the support of OpenMP library.
• A generic function plot() is added for plotting the network structures among the identified genetic variants.

regnet 0.4.0 [2019-6-7]

Based on users’ feedback, we have

• Added more checking steps for data format, which help users make sure their data are in the correct format.
• Provided more information in the documentation for troubleshooting.

regnet 0.3.0 [2018-5-21]

• Two new, easy to use, integrated interfaces: cv.regnet() and regnet().
• New methods for continuous and survival responses.
• The new “clv” argument allows the presence of clinical variables that are not subject to penalty in the X matrix.

regnet 0.2.0 [2017-10-14]

• Provides c++ implementation for coordinate descent algorithms. This update significantly increases the speed of cross-validation functions in this package.

Methods

This package provides implementation for methods proposed in

• Ren, J., He, T., Li, Y., Liu, S., Du, Y., Jiang, Y., Wu, C. (2017). Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes. BMC Genetics, 18(1):44

• Ren, J., Du, Y., Li, S., Ma, S., Jiang,Y. and Wu, C. (2019). Robust network-based regularization and variable selection for high dimensional genomics data in cancer prognosis. Genet. Epidemiol. 43:276-291

References

• Wu, C., and Ma, S. (2015). A selective review of robust variable selection with applications in bioinformatics. Briefings in Bioinformatics, 16(5), 873–883

• Wu, C., Shi, X., Cui, Y. and Ma, S. (2015). A penalized robust semiparametric approach for gene-environment interactions. Statistics in Medicine, 34 (30): 4016–4030

• Wu, C, Jiang, Y, Ren, J, Cui, Y, Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.Statistics in Medicine, 37:437–456