RLassoCox-package {RLassoCox} | R Documentation |
RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.
The DESCRIPTION file:
Package: | RLassoCox |
Type: | Package |
Title: | A reweighted Lasso-Cox by integrating gene interaction information |
Version: | 1.1.0 |
Date: | 2020-10-21 |
Authors@R: | c(person(given = "Wei", family = "Liu", email = "freelw@qq.com", role = c("cre", "aut"),comment = c(ORCID = "0000-0002-5496-3641"))) |
Depends: | R (>= 4.1), glmnet |
Imports: | Matrix, igraph, survival, stats |
Description: | RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. |
License: | Artistic-2.0 |
biocViews: | Survival, Regression, GeneExpression, GenePrediction, Network |
BugReports: | https://github.com/weiliu123/RLassoCox/issues |
BiocType: | Software |
Suggests: | knitr |
VignetteBuilder: | knitr |
git_url: | https://git.bioconductor.org/packages/RLassoCox |
git_branch: | master |
git_last_commit: | 2c035f5 |
git_last_commit_date: | 2021-05-19 |
Date/Publication: | 2021-05-27 |
Author: | Wei Liu [cre, aut] (<https://orcid.org/0000-0002-5496-3641>) |
Maintainer: | Wei Liu <freelw@qq.com> |
Index of help topics:
RLassoCox Reweighted Lasso-Cox model RLassoCox-package A reweighted Lasso-Cox by integrating gene interaction information cvRLassoCox Cross-validation for the RLasso-Cox model dGMMirGraph The KEGG network mRNA_matrix The expression data predict.RLassoCox Make predictions from a RLasso-Cox model predict.cvRLassoCox Make predictions from a cross-validated RLasso-Cox model rw Directed Random Walk survData Survival data
Very simple to use. Accepts x,y data for the RLasso-Cox model, and makes predictions for new samples.
RLassoCox
A rewighted Lasso-Cox model for survival prediction and
biomarker discovery.
predict.RLassoCox
This function predicts the risk of new samples
from a fitted RLasso-Cox
model.
cvRLassoCox
Does k-fold cross-validation for the RLasso-Cox model,
produces a plot, and returns a value for lambda
predict.cvRLassoCox
This function makes predictions from a
cross-validated RLasso-Cox model, using the optimal value chosen for lambda.
NA
Maintainer: NA
Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction. To be published.
library("survival") library("igraph") library("glmnet") library("Matrix") data(dGMMirGraph) data(mRNA_matrix) data(survData) trainSmpl.Idx <- sample(1:dim(mRNA_matrix)[1], floor(2/3*dim(mRNA_matrix)[1])) testSmpl.Idx <- setdiff(1:dim(mRNA_matrix)[1], trainSmpl.Idx) trainSmpl <- mRNA_matrix[trainSmpl.Idx ,] testSmpl <- mRNA_matrix[testSmpl.Idx ,] res <- RLassoCox(x=trainSmpl, y=survData[trainSmpl.Idx ,], globalGraph=dGMMirGraph) lp <- predict(object = res, newx = testSmpl) cv.res <- cvRLassoCox(x=trainSmpl, y=survData[trainSmpl.Idx ,], globalGraph=dGMMirGraph, nfolds = 5) cv.lp <- predict(object = cv.res, newx = testSmpl, s = "lambda.min")