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# RLassoCox
A reweighted Lasso-Cox model for survival prediction and biomarker discovery.
# Details
Package: RLassoCox
Type: Package
Title: A reweighted Lasso-Cox by integrating gene interaction information
Version: 0.99.4
Date: 2020-11-20
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
# Index of help topics:
RLassoCox Reweighted Lasso-Cox model
cvRLassoCox Cross-validation for the RLasso-Cox model
dGMMirGraph The KEGG network
g.HuRI.EntrezID The HuRI 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
# Examples
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")