group_mappoly: Assign markers to linkage groups
In mappoly: Genetic Linkage Maps in Autopolyploids

group_mappoly

R Documentation

Assign markers to linkage groups

Description

Identifies linkage groups of markers using the results of two-point (pairwise) analysis.

Usage

group_mappoly(
  input.mat,
  expected.groups = NULL,
  inter = TRUE,
  comp.mat = FALSE,
  LODweight = FALSE,
  verbose = TRUE
)

Arguments

`input.mat`	an object of class `mappoly.rf.matrix`
`expected.groups`	when available, inform the number of expected linkage groups (i.e. chromosomes) for the species
`inter`	if `TRUE` (default), plots a dendrogram highlighting the expected groups before continue
`comp.mat`	if `TRUE`, shows a comparison between the reference based and the linkage based grouping, if the chromosome information is available (default = FALSE)
`LODweight`	if `TRUE`, clusterization is weighted by the square of the LOD Score
`verbose`	logical. If `TRUE` (default), current progress is shown; if `FALSE`, no output is produced

Value

Returns an object of class mappoly.group, which is a list containing the following components:

`data.name`	the referred dataset name
`hc.snp`	a list containing information related to the UPGMA grouping method
`expected.groups`	the number of expected linkage groups
`groups.snp`	the groups to which each of the markers belong
`seq.vs.grouped.snp`	comparison between the genomic group information (when available) and the groups provided by `group_mappoly`
`chisq.pval.thres`	the threshold used on the segregation test when reading the dataset
`chisq.pval`	the p-values associated with the segregation test for all markers in the sequence

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

References

Mollinari, M., and Garcia, A. A. F. (2019) Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models, _G3: Genes, Genomes, Genetics_. doi: 10.1534/g3.119.400378

Examples

    ## Getting first 20 markers from two linkage groups
    all.mrk <- make_seq_mappoly(hexafake, c(1:20,601:620))
    red.mrk <- elim_redundant(all.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)
    counts <- cache_counts_twopt(unique.mrks, cached = TRUE)
    all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                                 count.cache = counts,
                                 ncpus = 1,
                                 verbose = TRUE)

    ## Full recombination fraction matrix
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full, index = FALSE)

    lgs <- group_mappoly(input.mat = mat.full,
                         expected.groups = 2,
                         inter = TRUE,
                         comp.mat = TRUE, #this data has physical information
                         verbose = TRUE)
    lgs
    plot(lgs)

mappoly documentation built on Jan. 6, 2023, 1:16 a.m.