Summarize CpGs methylation information over genomic regions
Source:R/summarize_meth_regions.R
summarize_meth_regions.RdRun summarizing functions on the CpGs in BED files across genomic regions.
Parallelized across files using threads from the "iscream.threads" option.
Usage
summarize_meth_regions(
bedfiles,
regions,
fun = "all",
aligner = "biscuit",
feature_col = NULL,
mval = TRUE,
set_region_rownames = FALSE,
nthreads = NULL
)Arguments
- bedfiles
A vector of BED file paths
- regions
A vector, data frame or GenomicRanges of genomic regions. See details.
- fun
Function(s) to apply over the region. See details.
- aligner
The aligner used to produce the BED files - one of "biscuit", "bismark", "bsbolt".
- feature_col
Column name of the input
regionsdata frame containing a name for each genomic region. Set only if the using a data frame as the input regions format. See details.- mval
Whether to calculate the M value (coverage \(\times \beta\)) or use the beta value when applying the function.
- set_region_rownames
Use the region strings as the returned data frame's rownames. Can be useful if you have a named regions and want both the regions strings rownames and the feature names. See details.
- nthreads
Set number of threads to use overriding the
"iscream.threads"option. See?set_threadsfor more information.
Supported functions
Sum:
"sum"Mean:
"mean"Median:
"median"Standard deviation:
"stddev"Variance:
"variance"Minimum:
"min"Maximum:
"max"Range:
"range"No. of records in the region:
"count"
The summarizing computations are backed by the Armadillo library. See https://arma.sourceforge.net/docs.html#stats_fns for futher details on the supported functions
Using feature identifiers
regions may be string vector in the form "chr:start-end", a GRanges
object or a data frame with "chr", "start", and "end" columns. The feature
column of the output will contain a "chr:start-end" identifier for each
summarized region. To use other identifiers, like a gene name for a region
instead of the coordinates, set the names of the vector or GRanges to those
identifiers. These names will be used instead of the genomic region string
to describe each feature in the output dataframe. If regions is a data
frame make an additional column with the identifiers and pass that column
name to feature_col. See examples.
Examples
# also see examples from ?summarize_regions
bedfiles <- system.file("extdata", package = "iscream") |>
list.files(pattern = "[a|b|c|d].bed.gz$", full.names = TRUE)
# make a vector of regions
regions <- c("chr1:1-6", "chr1:7-10", "chr1:11-14")
summarize_meth_regions(bedfiles, regions)
#> [13:21:20.825015] [iscream::summarize_regions] [info] Summarizing 3 regions from 4 bedfiles
#> [13:21:20.825056] [iscream::summarize_regions] [info] using sum, mean, median, stddev, variance, min, max, range, count
#> [13:21:20.825060] [iscream::summarize_regions] [info] with columns 4, 5 as coverage, M
#> feature file coverage.sum M.sum coverage.mean M.mean coverage.median
#> 1 chr1:1-6 a 4 2 1.333333 0.6666667 1.0
#> 2 chr1:7-10 a 3 1 1.500000 0.5000000 1.5
#> 3 chr1:11-14 a 5 5 2.500000 2.5000000 2.5
#> 4 chr1:1-6 b 4 2 2.000000 1.0000000 2.0
#> 5 chr1:7-10 b 1 1 1.000000 1.0000000 1.0
#> 6 chr1:11-14 b 3 1 1.500000 0.5000000 1.5
#> 7 chr1:1-6 c 2 2 2.000000 2.0000000 2.0
#> 8 chr1:7-10 c 3 1 1.500000 0.5000000 1.5
#> 9 chr1:11-14 c NA NA NA NA NA
#> 10 chr1:1-6 d 3 3 1.500000 1.5000000 1.5
#> 11 chr1:7-10 d 3 1 1.500000 0.5000000 1.5
#> 12 chr1:11-14 d 1 1 1.000000 1.0000000 1.0
#> M.median coverage.stddev M.stddev coverage.variance M.variance coverage.min
#> 1 1.0 0.5773503 0.5773503 0.3333333 0.3333333 1
#> 2 0.5 0.7071068 0.7071068 0.5000000 0.5000000 1
#> 3 2.5 0.7071068 0.7071068 0.5000000 0.5000000 2
#> 4 1.0 0.0000000 1.4142136 0.0000000 2.0000000 2
#> 5 1.0 0.0000000 0.0000000 0.0000000 0.0000000 1
#> 6 0.5 0.7071068 0.7071068 0.5000000 0.5000000 1
#> 7 2.0 0.0000000 0.0000000 0.0000000 0.0000000 2
#> 8 0.5 0.7071068 0.7071068 0.5000000 0.5000000 1
#> 9 NA NA NA NA NA NA
#> 10 1.5 0.7071068 0.7071068 0.5000000 0.5000000 1
#> 11 0.5 0.7071068 0.7071068 0.5000000 0.5000000 1
#> 12 1.0 0.0000000 0.0000000 0.0000000 0.0000000 1
#> M.min coverage.max M.max coverage.range M.range cpg_count
#> 1 0 2 1 1 1 3
#> 2 0 2 1 1 1 2
#> 3 2 3 3 1 1 2
#> 4 0 2 2 0 2 2
#> 5 1 1 1 0 0 1
#> 6 0 2 1 1 1 2
#> 7 2 2 2 0 0 1
#> 8 0 2 1 1 1 2
#> 9 NA NA NA NA NA NA
#> 10 1 2 2 1 1 2
#> 11 0 2 1 1 1 2
#> 12 1 1 1 0 0 1
names(regions) <- c("A", "B", "C")
summarize_meth_regions(bedfiles, regions, fun = c("mean", "stddev"), mval = FALSE)
#> [13:21:20.843732] [iscream::summarize_regions] [info] Summarizing 3 regions from 4 bedfiles
#> [13:21:20.843746] [iscream::summarize_regions] [info] using mean, stddev
#> [13:21:20.843749] [iscream::summarize_regions] [info] with columns 4, 5 as coverage, beta
#> feature file coverage.mean beta.mean coverage.stddev beta.stddev
#> 1 A a 1.333333 0.6666667 0.5773503 0.5773503
#> 2 B a 1.500000 0.2500000 0.7071068 0.3535534
#> 3 C a 2.500000 1.0000000 0.7071068 0.0000000
#> 4 A b 2.000000 0.5000000 0.0000000 0.7071068
#> 5 B b 1.000000 1.0000000 0.0000000 0.0000000
#> 6 C b 1.500000 0.5000000 0.7071068 0.7071068
#> 7 A c 2.000000 1.0000000 0.0000000 0.0000000
#> 8 B c 1.500000 0.5000000 0.7071068 0.7071068
#> 9 C c NA NA NA NA
#> 10 A d 1.500000 1.0000000 0.7071068 0.0000000
#> 11 B d 1.500000 0.2500000 0.7071068 0.3535534
#> 12 C d 1.000000 1.0000000 0.0000000 0.0000000
summarize_meth_regions(bedfiles, regions, fun = "sum")
#> [13:21:20.848055] [iscream::summarize_regions] [info] Summarizing 3 regions from 4 bedfiles
#> [13:21:20.848069] [iscream::summarize_regions] [info] using sum
#> [13:21:20.848072] [iscream::summarize_regions] [info] with columns 4, 5 as coverage, M
#> feature file coverage.sum M.sum
#> 1 A a 4 2
#> 2 B a 3 1
#> 3 C a 5 5
#> 4 A b 4 2
#> 5 B b 1 1
#> 6 C b 3 1
#> 7 A c 2 2
#> 8 B c 3 1
#> 9 C c NA NA
#> 10 A d 3 3
#> 11 B d 3 1
#> 12 C d 1 1