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Find the largest subset of markers such that no two adjacent markers are separated by less than some distance.
reduce_markers(
map,
min_distance = 1,
weights = NULL,
max_batch = 10000,
batch_distance_mult = 1,
cores = 1
)A list like the input map, but with the selected
subset of markers.
A list with each component being a vector with the marker positions for a chromosome.
Minimum distance between markers.
A (optional) list of weights on the markers; same
size as map.
Maximum number of markers to consider in a batch
If working with batches of markers,
reduce min_distance by this multiple.
Number of CPU cores to use, for parallel calculations.
(If 0, use parallel::detectCores().)
Alternatively, this can be links to a set of cluster sockets, as
produced by parallel::makeCluster().
Uses a dynamic programming algorithm to find, for each
chromosome, the subset of markers for with max(weights) is
maximal, subject to the constraint that no two adjacent markers may
be separated by more than min_distance.
The computation time for the algorithm grows with like the square
of the number of markers, like 1 sec for 10k markers
but 30 sec for 50k markers. If the number of markers on a chromosome
is greater than max_batch, the markers are split into batches and
the algorithm applied to each batch with min_distance smaller by a
factor min_distance_mult, and then merged together for one last pass.
Broman KW, Weber JL (1999) Method for constructing confidently ordered linkage maps. Genet Epidemiol 16:337--343
find_dup_markers(), drop_markers()
# read data
grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
# grab genetic map
gmap <- grav2$gmap
# subset to markers that are >= 1 cM apart
gmap_sub <- reduce_markers(gmap, 1)
# drop all of the other markers from the cross
markers2keep <- unlist(lapply(gmap_sub, names))
grav2_sub <- pull_markers(grav2, markers2keep)
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