find_blocks | R Documentation |
Function to allocate markers into linkage blocks. This is an EXPERIMENTAL FUNCTION and should be used with caution.
find_blocks(
input.seq,
clustering.type = c("rf", "genome"),
rf.limit = 1e-04,
genome.block.threshold = 10000,
rf.mat = NULL,
ncpus = 1,
ph.thres = 3,
phase.number.limit = 10,
error = 0.05,
verbose = TRUE,
tol = 0.01,
tol.err = 0.001
)
input.seq |
an object of class |
clustering.type |
if |
rf.limit |
the maximum value to consider linked markers in
case of |
genome.block.threshold |
the threshold to assume markers are in the same linkage block.
to be considered when allocating markers into blocks in case of |
rf.mat |
an object of class |
ncpus |
Number of parallel processes to spawn |
ph.thres |
the threshold used to sequentially phase markers.
Used in |
phase.number.limit |
the maximum number of linkage phases of the sub-maps.
The default is 10. See |
error |
the assumed global genotyping error rate. If |
verbose |
if |
tol |
tolerance for the C routine, i.e., the value used to evaluate convergence. |
tol.err |
tolerance for the C routine, i.e., the value used to evaluate convergence, including the global genotyping error in the model. |
a list containing 1: a list of blocks in form of mappoly.map
objects;
2: a vector containing markers that were not included into blocks.
Marcelo Mollinari, mmollin@ncsu.edu
## Not run:
## Selecting 50 markers in chromosome 5
s5 <- make_seq_mappoly(tetra.solcap, "seq5")
s5 <- make_seq_mappoly(tetra.solcap, s5$seq.mrk.names[1:50])
tpt5 <- est_pairwise_rf(s5)
m5 <- rf_list_to_matrix(tpt5, 3, 3)
fb.rf <- find_blocks(s5, rf.mat = m5, verbose = FALSE, ncpus = 2)
bl.rf <- fb.rf$blocks
plot_map_list(bl.rf)
## Merging resulting maps
map.merge <- merge_maps(bl.rf, tpt5)
plot(map.merge, mrk.names = T)
## Comparing linkage phases with pre assembled map
id <- na.omit(match(map.merge$info$mrk.names, solcap.err.map[[5]]$info$mrk.names))
map.orig <- get_submap(solcap.err.map[[5]], mrk.pos = id)
p1.m<-map.merge$maps[[1]]$seq.ph$P
p2.m<-map.merge$maps[[1]]$seq.ph$Q
names(p1.m) <- names(p2.m) <- map.merge$info$mrk.names
p1.o<-map.orig$maps[[1]]$seq.ph$P
p2.o<-map.orig$maps[[1]]$seq.ph$Q
names(p1.o) <- names(p2.o) <- map.orig$info$mrk.names
n <- intersect(names(p1.m), names(p1.o))
plot_compare_haplotypes(4, p1.o[n], p2.o[n], p1.m[n], p2.m[n])
### Using genome
fb.geno <- find_blocks(s5, clustering.type = "genome", genome.block.threshold = 10^4)
plot_map_list(fb.geno$blocks)
splt <- lapply(fb.geno$blocks, split_mappoly, 1)
plot_map_list(splt)
## End(Not run)
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