Description Usage Arguments Value Author(s) See Also Examples
create_region_object
creates genomic regions (e.g. forms
methylation regions data) using as input methylation and annotation data
with genomic regions of interest.
1 2 3 4 5 6 7 8 9 10 | create_region_object(
met_dt,
anno_dt,
cov = 5,
sd_thresh = 0.1,
ignore_strand = TRUE,
filter_empty_region = TRUE,
fmin = -1,
fmax = 1
)
|
met_dt |
A |
anno_dt |
A |
cov |
Integer defining the minimum coverage of CpGs that each region must contain. |
sd_thresh |
Optional numeric defining the minimum standard deviation of the methylation change in a region. This is used to filter regions with no methylation variability. |
ignore_strand |
Logical, whether or not to ignore strand information. |
filter_empty_region |
Logical, whether to discard genomic regions that have no CpG coverage or do not pass filtering options. |
fmin |
Minimum range value for location scaling. Under this version, it should be left to its default value -1. |
fmax |
Maximum range value for location scaling. Under this version, it should be left to its default value 1. |
A list
object containing the two elements:
met
: A list containing methylation region data, where each entry in
the list is an L_{i} X D dimensional matrix, where L_{i}
denotes the number of CpGs found in region i
. The columns contain
the following information:
1st column: Contains the locations of CpGs relative to centre. Note that the actual locations are scaled to the (fmin, fmax) region.
2nd column: If "bulk" data (i.e. binomial) it contains the total number of reads at each CpG location, otherwise the methylation level.
3rd column: If "bulk" data, the methylated reads at each CpG location, otherwise this D = 2 and this column is absent.
. Rownames of each matrix contain the actual CpG genomic coordinates as <chr>:<location>.
anno
: The annotation
object.
Note: The lengths of met
and anno
should match.
C.A.Kapourani C.A.Kapourani@ed.ac.uk
1 2 3 4 5 6 7 8 9 10 11 | ## Not run:
# Download the files and change the working directory to that location
met_dt <- read_met("name_of_met_file")
anno_dt <- read_anno("name_of_anno_file")
obj <- create_region_object(met_dt, anno_dt)
# Extract methylation regions
met <- obj$met
## End(Not run)
|
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