Generate all crosses from given parent set.

Description

Generate all crosses from given parent set.

Usage

allCrosses(n, line_id)

Arguments

Value

List of crossing procedure. The output value is defined by "multiPhylo" class.

Note

This function is adapted and modified from 'allTrees', 'dfactorial', and 'ldfactorial' functions in the R package 'phangorn' (GPL-2 | GPL-3). See: https://cran.r-project.org/web/packages/phangorn/index.html

References

K.P. Schliep (2011) phangorn: phylogenetic analysis in R

Examples

n <- 3
line_id <- c("x1", "x2", "x7")
rslt <- allCrosses(n, line_id)
plot(rslt[[1]])
plot(rslt[[2]])
plot(rslt[[3]])

Function to calculate N_total by the algorithm proposed by Sevin et al. (2004)

Description

Function to calculate N_total by the algorithm proposed by Sevin et al. (2004)

Usage

calcCost(
  topolo,
  gene_df1_sel,
  gene_df2_sel,
  recom_mat,
  prob_total,
  last_cross = FALSE,
  last_selfing = FALSE
)

Arguments

Value

'calCost' function returns the list of 'n_plant_df', 'gene_df1_sel' and 'gene_df2_sel'. 'n_plant_df' contains the information about the number of progenies should be produced at each crossing (node in the tree).

Function to calculate the number of necessary individuals and generations as the crossing cost for all the crossing schemes. This is the wrapper function of calCost.

Description

Function to calculate the number of necessary individuals and generations as the crossing cost for all the crossing schemes. This is the wrapper function of calCost.

Usage

calcCostAll(
  line_comb_lis,
  gene_df1,
  gene_df2,
  recom_mat,
  prob_total,
  last_cross = FALSE,
  last_selfing = FALSE
)

Arguments

Value

calcCostAll function returns a 'gpyramid_all' object. This is a named list with the following components: * 'cost_all' ('data frame'): Data frame showing the number of necessary generations (n_generation) and the number of individuals (N_total) for each crossing scheme. The crossing schemes are identified by cross_id. * Other components mirror the input parameters for downstream analysis.

Find parent sets from the candidate cultivars

Description

Find parent sets from the candidate cultivars

Usage

findPset(gene_df1, gene_df2, line_id)

Arguments

Value

'findPset()' returns a list of parental cultivar sets. Each element of list contains the parent names.

Examples

gene_df1 <-
 data.frame(x1 = c(1, 1, 1, 1, 1, 0, 0),
            x2 = c(1, 1, 1, 0, 1, 1, 0),
            x3 = c(1, 1, 1, 0, 1, 1, 0),
            x4 = c(1, 1, 0, 0, 0, 0, 0),
            x5 = c(0, 0, 1, 0, 1, 1, 0),
            x6 = c(0, 0, 1, 1, 0, 0, 0),
            x7 = c(0, 1, 1, 0, 0, 0, 1))
gene_df2 <-
 data.frame(x1 = c(0, 0, 0, 0, 0, 0, 0),
            x2 = c(0, 0, 0, 0, 0, 1, 0),
            x3 = c(1, 0, 0, 0, 0, 0, 0),
            x4 = c(1, 1, 0, 0, 0, 0, 0),
            x5 = c(0, 0, 1, 0, 1, 1, 0),
            x6 = c(0, 0, 1, 1, 0, 0, 0),
            x7 = c(0, 1, 1, 0, 0, 0, 1))

line_id <- c("x1", "x2", "x3", "x4", "x5", "x6", "x7")

findPset(gene_df1, gene_df2, line_id)

Function to get one crossing scheme from all the crossing schemes.

Description

Function to get one crossing scheme from all the crossing schemes.

Usage

getFromAll(gpyramid_all_obj, cross_id)

Arguments

Value

getFromAll function returns a 'getFromAll' object. This is a named list with the following components: * 'topolo' ('phylo'): crossing scheme described as the phylo object, which is defined by ape package. * 'last_cross' ('boolean'): Whether or not to conduct the last cross to a cultivar without target alleles. * 'last_selfint' ('boolean'): Whether or not to conduct the last selfing. * 'cost_onecrossing' ('list'): List of output information about one gene pyramiding scheme. The object has class 'getFromAll' and can be summarized using 'summary()'.

Util fuunction to generate haplotype dataframe from raw data.

Description

Util fuunction to generate haplotype dataframe from raw data.

Usage

util_haplo(in_df, target, non_target, hetero, line_cul)

Arguments

Value

A list of genotype and line names for downstream analysis. This is a list with the following components: * 'gene_df1' ('data frame'): One set of haplotype. Values take 1 (target allele) or 0 (non-target). * 'gene_df2' ('data frame'): The other set of haplotype. Values take 1 or 0. * 'line_id' ('vector'): Line names.

Examples

in_df <- data.frame(line = c("CV1", "CV2", "CV3", "CV4", "CV5"),
                    gene1 = c("A", "A", "B", "B", "A"),
                    gene2 = c("B", "A", "A", "B", "H"),
                    gene3 = c("A", "A", "H", "H", "A"),
                    gene4 = c("A", "B", "H", "A", "B"))

util_haplo(in_df, target = "A", non_target = "B", hetero = "H", line_cul = "line")

Util function to generate recom_mat from raw data. It returens the recombination probability based on Halden's map function

Description

Util function to generate recom_mat from raw data. It returens the recombination probability based on Halden's map function

Usage

util_recom_mat(in_df, unit = "cM")

Arguments

Value

util_recom_mat function returns a matrix of recombination probability between each combination of genes.

References

Haldane (1919) The combination of linkage values, and the calculation of distances between the loci of linked factors. Journal of Genetics 8: 299-309.

Examples

in_df <- data.frame(Gene = c("g1", "g2", "g3", "g4"),
                    Chr = c("1A", "1B", "2A", "2A"),
                    cM = c(30, 50, 35, 50))

util_recom_mat(in_df)