IdentifyMCB {EnMCB} | R Documentation |
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 between
the beta values of any two CpGs < 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.
IdentifyMCB( MethylationProfile, method = c("pearson", "spearman", "kendall")[1], CorrelationThreshold = 0.8, PositionGap = 1000, platform = "Illumina Methylation 450K" )
MethylationProfile |
Methylation matrix is used in the analysis. |
method |
method used for calculation of correlation, |
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. |
Currently, only illumina 450k platform is supported, the methylation profile need to convert into matrix format.
Object of class list
with elements:
MCBsites | Character set contains all CpG sites in MCBs. |
MCBinformation | Matrix contains the information of results. |
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)
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