data-raw/02-create_bsseq_rda.R

In sartorlab/methylSig: MethylSig: Differential Methylation Testing for WGBS and RRBS Data

library(bsseq)
library(GenomicRanges)

########################################

bis_cov_file1 = './inst/extdata/bis_cov1.cov'
bis_cov_file2 = './inst/extdata/bis_cov2.cov'
bis_cov_file3 = './inst/extdata/bis_cov3.cov'
bis_cov_file4 = './inst/extdata/bis_cov4.cov'

########################################

#---------CG-------------CG-------------CG--------CG--------C--------------CG
# test1 coverage
#         5              30             10        0         40             1000
#          5              70             20        0                         1500
# test2 coverage
#         10             50             15        5         20             100
#          10             50             35        5                         200
# test1 methylation
#         4              0              9         0         35             900
#          4              5              19        0                         1400
# test2 methylation
#         9              1              14        5         15             99
#          9              5              34        5                         199
bsseq_stranded = read.bismark(
    files = c(bis_cov_file1, bis_cov_file2),
    colData = data.frame(row.names = c('test1','test2')),
    rmZeroCov = FALSE,
    strandCollapse = FALSE
)

########################################

#---------C--------------C--------------C---------C---------C--------------C-
# test1 coverage
#         10             100            30        0         40             2500
# test2 coverage
#         20             100            50        10        20             300
# test1 methylation
#         8              5              28        0         35             2300
# test2 methylation
#         18             6              48        10        15             298
bsseq_destranded = read.bismark(
    files = c(bis_cov_file3, bis_cov_file4),
    colData = data.frame(row.names = c('test3','test4')),
    rmZeroCov = FALSE,
    strandCollapse = FALSE
)

########################################

cov = matrix(c(
    10,20,30,90,
    40,50,60,100
), ncol = 2)

meth = matrix(c(
    10,20,30,90,
    40,50,60,100
), ncol = 2)

gr = GRanges(
    seqnames = c('chr1','chr1','chr1','chr2'),
    ranges = IRanges(
        start = c(10, 20, 30, 10),
        end = c(10, 20, 30, 10)
    )
)

#---------C---------C---------C
# test1 coverage / methylation
#          10        20        30
# test2 coverage / methylation
#          40        50        60
#---------C
# test1 coverage / methylation
#          90
# test2 coverage / methylation
#          100

bsseq_multichrom = BSseq(
    Cov = cov,
    M = meth,
    gr = gr,
    pData = data.frame(row.names = c('test1','test2')),
    sampleNames = c('test1','test2')
)

########################################

# Extract chr21 and chr22 hg19 promoters
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene
seqlevels(txdb) = c('chr21', 'chr22')
promoters_gr = promoters(txdb, upstream=1000, downstream=400)
promoters_gr = reduce(promoters_gr)

########################################

usethis::use_data(
    bsseq_stranded,
    bsseq_destranded,
    bsseq_multichrom,
    promoters_gr,
    overwrite = TRUE)