Count the number of overlapping fragments.
Usage
getFragmentOverlaps(
x,
barcodes = NULL,
regionsToExclude = GRanges(c("M", "chrM", "MT", "X", "Y", "chrX", "chrY"), IRanges(1L,
width = 10^8)),
minFrags = 500L,
uniqueFrags = TRUE,
maxFragSize = 1000L,
removeHighOverlapSites = TRUE,
fullInMemory = FALSE,
BPPARAM = NULL,
verbose = TRUE,
ret = c("stats", "loci", "coverages")
)Arguments
- x
The path to a fragments file, or a GRanges object containing the fragments (with the `name` column containing the barcode, and optionally the `score` column containing the count).
- barcodes
Optional character vector of cell barcodes to consider
- regionsToExclude
A GRanges of regions to exclude. As per the original Amulet method, we recommend excluding repeats, as well as sex and mitochondrial chromosomes.
- minFrags
Minimum number of fragments for a barcode to be considered. If `uniqueFrags=TRUE`, this is the minimum number of unique fragments. Ignored if `barcodes` is given.
- uniqueFrags
Logical; whether to use only unique fragments.
- maxFragSize
Integer indicating the maximum fragment size to consider
- removeHighOverlapSites
Logical; whether to remove sites that have more than two reads in unexpectedly many cells.
- fullInMemory
Logical; whether to process all chromosomes together. This will speed up the process but at the cost of a very high memory consumption (as all fragments will be loaded in memory). This is anyway the default mode when `x` is not Tabix-indexed.
- BPPARAM
A `BiocParallel` parameter object for multithreading. Note that multithreading will increase the memory usage.
- verbose
Logical; whether to print progress messages.
- ret
What to return, either barcode 'stats' (default), 'loci', or 'coverages'.
Details
When used on normal (or compressed) fragment files, this implementation is relatively fast (except for reading in the data) but it has a large memory footprint since the overlaps are performed in memory. It is therefore recommended to compress the fragment files using bgzip and index them with Tabix; in this case each chromosome will be read and processed separately, leading to a considerably lower memory footprint.