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)
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. 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.
To install from github, run these two lines of code in R
install.packages("devtools")
devtools::install_github("jrhub/regnet")
Released versions of regnet are available on R CRAN (link), and can be installed within R via
install.packages("regnet")
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$lambda
b = regnet(X, Y, "survival", "network", out$lambda[1,1], out$lambda[1,2], clv=clv, robust=TRUE)
index = which(rgn.surv$beta != 0)
pos = which(b != 0)
tp = length(intersect(index, pos))
fp = length(pos) - tp
list(tp=tp, fp=fp)
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$lambda
b = regnet(X, Y, "binary", "network", out$lambda[1,1], out$lambda[1,2], r = 4.5)
index = which(rgn.logi$beta != 0)
pos = which(b != 0)
tp = length(intersect(index, pos))
fp = length(pos) - tp
list(tp=tp, fp=fp)
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., Chu, G., Ma, S., Jiang,Y. and Wu, C. (2018). Robust network based regularization and variable selection for high dimensional genomics data in cancer prognosis.
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