README.md

In geneprophet/BSDMR: a region-based method for quantitating the conservation and divergence of methylation patterns between species by high order features

BSDMR

The goal of BSDMR is to find DMRs or CMRs by high order methylation profiles between species.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("geneprophet/BSDMR")

Example

This is a basic example which shows you how to use the package:

#library the package
library(BSDMR)

#example pipeline

#step1. read files
##read methylation report (the result of bismark)
file <- system.file("extdata", "example_human_chr22_CpG_report.txt", package = "BSDMR")
human_met <- read_methylation_report(file,min_coverage=4,type = "CG")
#> reading methylation report file ...
file <- system.file("extdata", "example_mouse_chr15_CpG_report.txt", package = "BSDMR")
mouse_met <- read_methylation_report(file,min_coverage=4,type = "CG")
#> reading methylation report file ...

##read annotation file (customized region)
file <- system.file("extdata", "human_chr22_annotation.txt", package = "BSDMR")
human_anno <- read_annotation(file)
#> reading annotation file...
file <- system.file("extdata", "mouse_chr15_annotation.txt", package = "BSDMR")
mouse_anno <- read_annotation(file)
#> reading annotation file...

#step2. cluster_sites_to_region
human_region <- cluster_sites_to_region(methylation = human_met,
                                        annotation = human_anno,
                                        min_sites_number = 10,
                                        max_distance_between_sites = 200,
                                        is_parallel = TRUE)

#step3. liftOver map the human_region to the annotated region of another species(mouse)
filePath <- system.file("extdata", "hg38ToMm10.over.chain", package = "BSDMR")
mouse_region <- change_genomic_coordinate(human_region,filePath,mouse_anno)

#step4. create region object as the input of modeling
human_obj <- create_region_object(human_met, human_region)
#> Creating methylation regions ...
mouse_obj <- create_region_object(mouse_met, mouse_region)
#> Creating methylation regions ...

#step5. infer profiles of reion object
human_basis_profile <- create_rbf_object(M = 8)
human_fit_profiles <- infer_profiles_vb(X = human_obj$met, basis = human_basis_profile, 
                                        is_parallel = TRUE, vb_max_iter = 100)
#> infering the methylation profiles by variational bayes....

human_basis_mean <- create_rbf_object(M = 0)
human_fit_mean <- infer_profiles_vb(X = human_obj$met, basis = human_basis_mean,
                                    is_parallel = TRUE, vb_max_iter = 100)
#> infering the methylation profiles by variational bayes....

mouse_basis_profile <- create_rbf_object(M = 8)
mouse_fit_profiles <- infer_profiles_vb(X = mouse_obj$met, basis = mouse_basis_profile, 
                                        is_parallel = TRUE, vb_max_iter = 100)
#> infering the methylation profiles by variational bayes....

mouse_basis_mean <- create_rbf_object(M = 0)
mouse_fit_mean <- infer_profiles_vb(X = mouse_obj$met, basis = mouse_basis_mean, 
                                    is_parallel = TRUE, vb_max_iter = 100)
#> infering the methylation profiles by variational bayes....

#step6. computer the adjusted consine distance of profiles and measure the similarity
similarity <- adjusted_cosine_similarity(queryProfiles = human_fit_profiles,subjectProfiles = mouse_fit_profiles)

#step7. visualization
which(similarity>0.9)
#>  [1] 160 170 419 439 442 483 525 535 565 573 577 644 651 679 744 771 801 831 838
#> [20] 873 971 989
i=160
plot_infer_profiles(region = i, obj_prof = human_fit_profiles,obj_mean = human_fit_mean,
                    obs = human_obj, title = paste0("Gene ID ",human_obj$anno$id[i]))

plot_infer_profiles(region = i, obj_prof = mouse_fit_profiles,obj_mean = mouse_fit_mean,
                    obs = mouse_obj, title = paste0("Gene ID ",mouse_obj$anno$id[i]))

geneprophet/BSDMR documentation built on March 3, 2021, 5:50 a.m.