genet_dist: Genetic distance
In RClone: Partially Clonal Populations Analysis

Description Usage Arguments Details Value Author(s) References Examples

Defining MLL (MultiLocus Lineage): ascertaining that each distinct MLG (MultiLocus Genotype) belongs to a distinct genet (Halkett et al., 2005a).

genet_dist(data1, haploid = FALSE, vecpop = NULL, manh = FALSE, manh_w = FALSE, 
graph = FALSE, breaking = NULL, alpha1 = NULL, alpha2 = NULL, export = FALSE)
genet_dist_sim(data1, haploid = FALSE, vecpop = NULL, nbrepeat = 1000, 
genet = FALSE, manh = FALSE, manh_w = FALSE, graph = FALSE, breaking = NULL,
export = FALSE)

`data1`	a `Rclone` table with one allele per column.
`haploid`	logical, option, `haploid` indicates the ploidy level of `data1`.
`vecpop`	vector, option, `vecpop` indicates the population name of each unit of `data1`, if `data1` contains several populations. If `data1` contains only one population, leave `vecpop = NULL`.
`manh`	option, if `TRUE`, computes genetic distances among MLG in terms of divergence of microsatellites motifs (Rozenfeld et al., 2007).
`manh_w`	option, if `TRUE`, computes genetic distances among MLG in terms of weighted divergence of microsatellites motifs (Rozenfeld et al., 2007).
`graph`	option, if `TRUE`, displays a barplot with `breaking` and `pas` arguments.
`breaking`	numeric, option, if `breaking != NULL`, adds `breaks` argument for barplot as `breaks = seq(0, max, X)`, with `X`, the numerical value of `breaking`.
`alpha1`	numeric, option, if `alpha1` is not NULL, a vertical significativity line is added on graph at `alpha1`
`alpha2`	numeric, option, if `alpha2` is not NULL, a vertical significativity line is added on graph at `alpha2`.
`nbrepeat`	numeric, the number of repeats for simulation (i.e. reproduction event).
`genet`	option, if `FALSE`, selfing is taking into account in simulation through ramets.
`export`	option, if `TRUE`, graph is saved as .eps into working directory.

genet_dist and genet_dist_sim help determining MLL, i.e. if slightly different MLG belong or not to the same lineage.

genet_dist computes genetic distances between pairs of units in terms of number of alleles (Chakraborty and Jin, 1993) by default.

If manh = TRUE or manh_w = TRUE, divergence of SSR motifs (Rozenfeld et al., 2007) is used as genetic distance.

These distance distributions help defining MLL with significativity of alpha: every pair under alpha could be ramets of a genet.

genet_dist_sim computes genetic distances but after a reproduction event between the units.

The simulated distance distribution allows to distinguish slightly differences due to somatic mutation or scoring errors by stacking the two distributions.

genet_dist returns:

distance_matrix, a dist object with genetic distances by pair of units.
potential_clones, a table containing names and genetic distances of pairs of units under alpha1 distribution or of maximal genetic distance of alpha2.
all_pairs, a table containing names and genetic distances of every pairs of units.
sign, the numeric value of alpha1 or alpha2.

If vecpop != NULL, a list for every population.

genet_dist_sim returns a dist object of genetic distances by pair of units after a sexual reproduction event.

Creator/Author: Diane Bailleul <diane.bailleul.pro@gmail.com>
Author: Sophie Arnaud-Haond <sophie.arnaud@ifremer.fr>
Contributor: Solenn Stoeckel

The R implementation of RClone was written by Diane Bailleul.

The design was inspired by GenClone program described in Arnaud-Haond & Belkhir (2007).

Chakraborty & Jin, 1993, Determination of relatedness between individuals using DNA-fingerprinting.

Arnaud-Haond et al., 2007, Standardizing methods to address clonality in population studies.

Rozenfeld et al., 2007, Spectrum of genetic diversity and networks of clonal populations.

data(posidonia)

res <- genet_dist(posidonia, manh = TRUE, graph = TRUE, alpha1 = 0.05)

#Combining functions:
res1 <- genet_dist(posidonia, manh = TRUE)$distance_matrix
res2 <- genet_dist_sim_core(posidonia, nbrepeat = 100, manh = TRUE, genet = TRUE)$distance_matrix

p1 <- hist(res1, freq = FALSE, col = rgb(0,0.4,1,1), breaks = seq(0, max(res1), 2))
p2 <- hist(res2, freq = FALSE, col = rgb(0.7,0.9,1,0.5), breaks = seq(0, max(res2), 2))

limx <- max(max(res1), max(res2))
plot(p1, col = rgb(0,0.4,1,1), freq = FALSE, xlim = c(0,limx))
plot(p2, col = rgb(0.7,0.9,1,0.5), freq = FALSE, add = TRUE)

#Other way:
p1 <- as.data.frame(table(res1))
p2 <- as.data.frame(table(res2))
barplot(p1$Freq/sum(p1$Freq), col=rgb(0,0.4,1,1), axis.lty = 1, 
names.arg = as.numeric(as.character(p1[,1])))
barplot(p2$Freq/sum(p2$Freq), col=rgb(0.7,0.9,1,0.5), add = TRUE)
title("Genetic distances between pairs of MLG")

#Adding a legend:
leg.txt <- c("original data","simulated data")
col <- c(rgb(0,0.4,1,1), rgb(0.7,0.9,1,0.5))
legend("topright", fill = col, leg.txt, plot = TRUE, bty = "o", box.lwd = 1.5, 
bg = "white")