This function is designed to remove/blacklist markers based on mean coverage information.
Filter targets: SNPs
Statistics: mean coverage ( The read depth of individual genotype is averaged across markers).
filter_coverage(
data,
interactive.filter = TRUE,
filter.coverage = NULL,
filename = NULL,
parallel.core = parallel::detectCores() - 1,
verbose = TRUE,
...
)(4 options) A file or object generated by radiator:
tidy data
Genomic Data Structure (GDS)
How to get GDS and tidy data ?
Look into tidy_genomic_data,
read_vcf or
tidy_vcf.
(optional, logical) Do you want the filtering session to
be interactive. Figures of distribution are shown before asking for filtering
thresholds.
Default: interactive.filter = TRUE.
(optional, string) 2 options:
character string filter.coverage = "outliers" will use as
thresholds the lower and higher outlier values in the box plot.
integers string filter.coverage = c(10, 200). For the
marker's mean coverage lower and upper bound.
Default: filter.coverage = NULL.
(optional, character)
Default: filename = NULL.
(optional) The number of core used for parallel
execution during import.
Default: parallel.core = parallel::detectCores() - 1.
(optional, logical) When verbose = TRUE
the function is a little more chatty during execution.
Default: verbose = TRUE.
(optional) Advance mode that allows to pass further arguments for fine-tuning the function. Also used for legacy arguments (see details or special section)
With interactive.filter = FALSE, a list in the global environment,
with 7 objects:
$tidy.filtered.mac
$whitelist.markers
$blacklist.markers
$mac.data
$filters.parameters
With interactive.filter = TRUE, a list with 4 additionnal objects are generated.
$distribution.mac.global
$distribution.mac.local
$mac.global.summary
$mac.helper.table
dots-dots-dots ... allows to pass several arguments for fine-tuning the function:
filter.common.markers (optional, logical).
Default: filter.common.markers = FALSE,
Documented in filter_common_markers.
filter.monomorphic (logical, optional) Should the monomorphic
markers present in the dataset be filtered out ?
Default: filter.monomorphic = TRUE.
Documented in filter_monomorphic.
path.folder: to write ouput in a specific path
(used internally in radiator).
Default: path.folder = getwd().
If the supplied directory doesn't exist, it's created.
To help choose a threshold for the local and global MAF use the interactive version.
2 steps in the interactive version:
Step 1. Visualization and helper table.
Step 2. Filtering markers based on mean coverage
if (FALSE) {
# The minumum
}