RLassoCox-package {RLassoCox} | R Documentation

## A reweighted Lasso-Cox by integrating gene interaction information

### 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.

### Details

The DESCRIPTION file:

Package: | RLassoCox
Type: | Package
Title: | A reweighted Lasso-Cox by integrating gene interaction information
Version: | 1.2.0
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: | RELEASE_3_14
git_last_commit: | 34c7cf4
git_last_commit_date: | 2021-10-26
Date/Publication: | 2021-10-26
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.

### Author(s)

NA

Maintainer: NA

### References

Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction. To be published.

### Examples

    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")

[Package RLassoCox version 1.2.0 Index]