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 linear, SVM or elastic-net model Predict values 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 "enet". 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" )