inst/doc/vignette.R

In EnMCB: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models

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# if(!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("EnMCB")

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library(EnMCB)

methylation_dataset<-create_demo()

res<-IdentifyMCB(methylation_dataset)


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MCB<-res$MCBinformation


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MCB<-MCB[MCB[,"CpGs_num"]>2,]


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# sample the dataset into training set and testing set
trainingset<-colnames(methylation_dataset) %in% sample(colnames(methylation_dataset),0.6*length(colnames(methylation_dataset)))

testingset<-!trainingset

#build the models
library(survival)
data(demo_survival_data)

models<-metricMCB(MCB,
                    training_set = methylation_dataset[,trainingset],
                    Surv = demo_survival_data[trainingset],
                    Method = "cox")

#select the best
onemodel<-models$best_cox_model[[2]]


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prediction_results<-predict(onemodel,
                            as.data.frame(t(methylation_dataset[,testingset]))
                            )

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# You can choose one of MCBs:
select_single_one=1

em<-ensemble_model(t(MCB[select_single_one,]),
                    training_set=methylation_dataset[,trainingset],
                    Surv_training=demo_survival_data[trainingset])
                    

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em_prediction_results<-ensemble_prediction(ensemble_model = em,
                    predition_data = methylation_dataset[,testingset])

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sessionInfo()