## ----setup, include=FALSE, cache=FALSE-------------------------------------------------- library(knitr) # set global chunk options opts_chunk$set(fig.path='figure/minimal-', fig.align='center', fig.show='hold') options(formatR.arrow=TRUE,width=90) ## ----Installation----------------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("RLassoCox") ## ----load package----------------------------------------------------------------------- library("RLassoCox") ## ----load data-------------------------------------------------------------------------- data(mRNA_matrix) # gene expression profiles data(survData) # survival information data(dGMMirGraph) # gene interaction network ## ----survData--------------------------------------------------------------------------- head(survData) ## ----Split data set--------------------------------------------------------------------- set.seed(20150122) train.Idx <- sample(1:dim(mRNA_matrix)[1], floor(2/3*dim(mRNA_matrix)[1])) test.Idx <- setdiff(1:dim(mRNA_matrix)[1], train.Idx) x.train <- mRNA_matrix[train.Idx ,] x.test <- mRNA_matrix[test.Idx ,] y.train <- survData[train.Idx,] y.test <- survData[test.Idx,] ## ----Train model------------------------------------------------------------------------ mod <- RLassoCox(x=x.train, y=y.train, globalGraph=dGMMirGraph) ## ----PT--------------------------------------------------------------------------------- head(mod$PT) ## ----Plot------------------------------------------------------------------------------- plot(mod$glmnetRes) ## ----Print------------------------------------------------------------------------------ print(mod$glmnetRes) ## ----coef------------------------------------------------------------------------------- head(coef(mod$glmnetRes, s = 0.2)) ## ----predict.RLassoCox------------------------------------------------------------------ lp <- predict(object = mod, newx = x.test, s = c(0.1, 0.2)) head(lp) ## ----cv.RLassoCox----------------------------------------------------------------------- cv.mod <- cvRLassoCox(x=x.train, y=y.train, globalGraph=dGMMirGraph, nfolds = 5) ## ----plot------------------------------------------------------------------------------- plot(cv.mod$glmnetRes, xlab = "log(lambda)") ## ----optimal lambda--------------------------------------------------------------------- cv.mod$glmnetRes$lambda.min cv.mod$glmnetRes$lambda.1se ## ----coef.min--------------------------------------------------------------------------- coef.min <- coef(cv.mod$glmnetRes, s = "lambda.min") coef.min ## ----features--------------------------------------------------------------------------- nonZeroIdx <- which(coef.min[,1] != 0) features <- rownames(coef.min)[nonZeroIdx] features features.coef <- coef.min[nonZeroIdx] names(features.coef) <- features features.coef ## ----prediction------------------------------------------------------------------------- lp <- predict.cvRLassoCox(object = cv.mod, newx = x.test, s = "lambda.min") lp ## ----SessionInfo------------------------------------------------------------------------ sessionInfo()