RLassoCox {RLassoCox}R Documentation

Reweighted Lasso-Cox model

Description

A rewighted Lasso-Cox model for survival prediction and biomarker discovery.

Usage

RLassoCox(x, y, globalGraph = NULL, Gamma = 0.3, DEBUG = TRUE, 
            standardize = TRUE, ...)

Arguments

x

a n x p matrix of gene expression measurements with n samples and p genes.

y

a n x 2 matrix of survival data. The two columns represent disease status 'status' and survival time 'time' respectively.

globalGraph

An igraph R object containing the interaction network.

Gamma

A numeric value. The restart probability in directed random walk. Default is Gamma = 0.3.

DEBUG

Logical. Should debugging information be shown.

standardize

Logical flag for x standardization, prior to fitting the model. Default is TRUE.

...

Arguments to be passed to glmnet in R package glmnet.

Details

RLassoCox integrates gene interaction information into the Lasso-Cox model for accurate survial prediction and biomarker discovery.

Value

glmnetRes

An object of class "glmnet"

PT

The topological weights of genes

Author(s)

Wei Liu

References

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

See Also

predict

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)

[Package RLassoCox version 1.2.0 Index]