DiffMCB: Differential expressed methylation correlated blocks

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

Differential expressed methylation correlated blocks

Description

This function is used to find the Methylation correlated blocks that differentially expressed between groups based on the attractor framework. This function calculates attractors of all the MCBs among the groups and find the attractor MCBs.

Usage

DiffMCB(
  methylation_matrix,
  class_vector,
  mcb_matrix = NULL,
  min.cpgsize = 5,
  pVals_num = 0.05,
  base_method = c("Fstat", "Tstat", "eBayes")[1],
  sec_method = c("ttest", "kstest")[1],
  ...
)

Arguments

methylation_matrix	methylation profile matrix.
class_vector	class vectors that indicated the groups.
mcb_matrix	dataframe or matrix results returned by IdentifyMCB function.
min.cpgsize	threshold for minimum CpGs must included in the individual MCBs.
pVals_num	p value threshold for the test.
base_method	base method used for calculation of differentially methylated regions, should be one of 'Fstat','Tstat','eBayes'. Defualt is Fstat.
sec_method	secondly method in attractor framework, should be one of 'kstest','ttest'. Defualt is ttest.
...	other parameters pass to the function.

Details

Currently, only illumina 450k platform is supported.
If you want to use other platform, please provide the annotation file with CpG's chromosome and loci.
The methylation profile need to convert into matrix format.

Value

Object of class list with elements:

global	Character set contains statistical value for all CpG sites in MCBs.
tab	Matrix contains the information of results.

Author(s)

Xin Yu

References

Xin Yu, De-Xin Kong, EnMCB: an R/bioconductor package for predicting disease progression based on methylation correlated blocks using ensemble models, Bioinformatics, 2021, btab415

Examples

data('demo_data', package = "EnMCB")
data('demo_survival_data', package = "EnMCB")
data('demo_MCBinformation', package = "EnMCB")
#Using survival censoring as group label just for demo, 
#this may replace with disease and control group in real use.
diffMCB_results <- DiffMCB(demo_data$realdata,demo_survival_data[,2], 
                           demo_MCBinformation,
                           pVals_num = 1)

whirlsyu/EnMCB documentation built on Jan. 25, 2023, 4:33 a.m.