metricMCB | 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,predict_time,ci,silent,alpha,n_mstop,n_nu,theta)

`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. |

`predict_time` |
time point of the ROC curve used in the AUC calculations, default is 5 years. |

`ci` |
if True, the confidence intervals for AUC under area under the receiver operating characteristic curve will be calculated. This will be time consuming. default is False. |

`silent` |
True indicates that processing information and progress bar will be shown. |

`alpha` |
The elasticnet mixing parameter, with 0 <= alpha <= 1. alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. |

`n_mstop` |
an integer giving the number of initial boosting iterations. If mstop = 0, the offset model is returned. |

`n_nu` |
a double (between 0 and 1) defining the step size or shrinkage parameter in mboost model. |

`theta` |
penalty used in the penalized coxph model, which is theta/2 time sum of squared coefficients. default is 1. |

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" )

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