EnMCB: an R/bioconductor package for predicting disease progression based on methylation correlated blocks using ensemble models.

MOTIVATION Based on the concept that adjacent CpG sites in the same DNA strand may be modified by a methyltransferase or demethylase together, current study found that the combination of multiple CpGs into a single block may improve cancer diagnosis. However, there is no R package available for building models based on methylation correlated blocks. RESULTS Here, we present a package named stacked ensemble of machine learning models for methylation correlated blocks (EnMCB) to build signatures based on DNA methylation correlated blocks for survival prediction. The Cox regression, support vector regression, mboost and elastic-net model were combined in the ensemble model. Methylation profiles from Cancer Genome Atlas were used as real datasets. The package automatically partitions the genome into blocks of tightly co-methylated CpG sites, termed methylation correlated blocks. After partitioning and modeling, the diagnostic capacities for predicting patients' survival are given. AVAILABILITY EnMCB is freely available for download at GitHub (https://github.com/whirlsyu/EnMCB/) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/EnMCB.html). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.