metricMCB {EnMCB} | R Documentation |
To enable quantitative analysis of the methylation patterns within individual Methylation Correlation Blocks across many samples, a single metric to define the methylated pattern of multiple CpG sites within each block. Compound scores which calculated all CpGs within individual Methylation Correlation Blocks by SVM model were used as the compound methylation values of Methylation Correlation Blocks.
metricMCB(MCBset,training_set,Surv,testing_set,Surv.new,Method,silent)
MCBset |
Methylation Correlation Block information returned by the IndentifyMCB function. |
training_set |
methylation matrix used for training the model in the analysis. |
Surv |
Survival function contain the survival information for training. |
testing_set |
methylation matrix used in the analysis. This can be missing then training set itself will be used as testing set. |
Surv.new |
Survival function contain the survival information for testing. |
Method |
model used to calculate the compound values for multiple Methylation correlation blocks. Options include "svm" "cox" and "lasso". The default option is SVM method. |
silent |
Ture indicates that processing information and progress bar will be shown. |
Object of class list
with elements (XXX will be replaced with the model name you choose):
MCB_XXX_matrix_training | Prediction results of model for training set. |
MCB_XXX_matrix_test_set | Prediction results of model for test set. |
XXX_auc_results | AUC results for each model. |
best_XXX_model | Model object for the model with best AUC. |
maximum_auc | Maximum AUC for the whole generated models. |
Xin Yu
Xin Yu et al. 2019 Predicting disease progression in lung adenocarcinoma patients based on methylation correlated blocks using ensemble machine learning classifiers (under review)
#import datasets data(demo_survival_data) datamatrix<-create_demo() data(demo_MCBinformation) #select MCB with at least 3 CpGs. demo_MCBinformation<-demo_MCBinformation[demo_MCBinformation[,"CpGs_num"]>2,] trainingset<-colnames(datamatrix) %in% sample(colnames(datamatrix),0.6*length(colnames(datamatrix))) testingset<-!trainingset #create the results using Cox regression. mcb_cox_res<-metricMCB(MCBset = demo_MCBinformation, training_set = datamatrix[,trainingset], Surv = demo_survival_data[trainingset], testing_set = datamatrix[,testingset], Surv.new = demo_survival_data[testingset], Method = "cox" )