IdentifyMCB: Identification of methylation correlated blocks

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

Description

This function is used to partition the genome into blocks of tightly co-methylated CpG sites, Methylation correlated blocks. This function calculates Pearson correlation coefficients r^2 between the beta values of any two CpGs r^2 < CorrelationThreshold was used to identify boundaries between any two adjacent markers indicating uncorrelated methylation. Markers not separated by a boundary were combined into MCB. Pearson correlation coefficients between two adjacent CpGs were calculated.

Usage

IdentifyMCB(
  MethylationProfile,
  method = c("pearson", "spearman", "kendall")[1],
  CorrelationThreshold = 0.8,
  PositionGap = 1000,
  platform = "Illumina Methylation 450K"
)

Arguments

MethylationProfile	Methylation matrix is used in the analysis.
method	method used for calculation of correlation, should be one of "pearson","spearman","kendall". Defualt is "pearson".
CorrelationThreshold	coef correlation threshold is used for define boundaries.
PositionGap	CpG Gap between any two CpGs positioned CpG sites less than 1000 bp (default) will be calculated.
platform	This parameter indicates the platform used to produce the methlyation profile.

Details

Currently, only illumina 450k platform is supported, the methylation profile need to convert into matrix format.

Value

Object of class list with elements:

MCBsites	Character set contains all CpG sites in MCBs.
MCBinformation	Matrix contains the information of results.

Author(s)

Xin Yu

References

Xin Yu et al. 2019 Predicting disease progression in lung adenocarcinoma patients based on methylation correlated blocks using ensemble machine learning classifiers (under review)

Examples

data('demo_data',package = "EnMCB")

#import the demo TCGA data with 10000+ CpGs site and 455 samples
#remove # to run
res<-IdentifyMCB(demo_data$realdata)
demo_MCBinformation<-res$MCBinformation

EnMCB documentation built on Dec. 22, 2020, 2 a.m.