RClone quickmanual: several populations
In RClone: Partially Clonal Populations Analysis

knitr::opts_chunk$set(collapse = TRUE, comment = ">", dev = 'pdf')

"Eager Beginners" Manual for RClone package

RClone data format: several populations

A. Introduction to RClone

RClone is a R package version of GenClone program (Arnaud-Haond & Belkhir 2007): to analyse data (SSR, SNP, ...), test for clonality and describe spatial clonal organisation.
Major improvements are multi-populations handling and definition of MLLs (Multilocus Lineages, i.e. slightly distinct Multi Locus Genotypes) through simulations.

RClone allows:

Description of data set
discrimination of MLG (MultiLocus Genotypes);
test for reliability of data (in terms of loci and sampling).
Determination of MLL (MultiLocus Lineages)
psex/psex Fis with pvalue computation;
genetic distance matrix computation and threshold definition.
Genotypic diversity and evenness indices calculation
Simpson complement;
Shannon-Wiener diversity and evenness indices;
Hill's Simpson reciprocal;
Pareto index.
Spatial organisation of MLG/MLL
spatial autocorrelation methods;
clonal subrange estimation;
Aggregation and Edge Effect indices estimation.

Some of these analysis can be applied to dataset with no repeated MLG, regardless of the reproductive system (sexual, partially asexual or strictly asexual).

B. RClone data format: several population

RClone functions works on diploid/haploid, one or several populations dataset.

If you have only one population in your dataset, go to other vignette RClone_quickmanual.

C. General format

If you have haploid data, you can skip to D. Description of data set.

To use RClone functions, your data table must look like:

library(RClone)
data(posidonia)

knitr::kable(posidonia[1:10,1:8], align = "c")

There is only one allele per column and, per locus, alleles are sorted by increasing order.

This is mandatory for all RClone functions.

As formatting can be source of error, we included functions to help formatting your diploid data:

1, The classic infile you could have, one locus per column

data(zostera)
head(zostera)

data(zostera)
knitr::kable(head(zostera), align = "c")

Zostera data is composed of:
a first column with population indication;
a second and third columns with x/y coordinates;
* a genotypic dataset.

popvec <- zostera[,1] #futur vecpop
coord_zostera <- zostera[,2:3] #futur coordinates
zostera <- zostera[,4:ncol(zostera)] #dataset

zostera <- convert_GC(zostera, 3) #We used "3" because this is the length of each allele.

head(zostera)

knitr::kable(zostera[1:6,1:7], align = "c")

2, The simple case: you already have a one-allele per column table

Just remove the pop/coords informations as above and sort your alleles:

sort_all(zostera)

3, You already work with Adegenet

Similar to case number 1, except you have to export your genind data into table first:

#library(adegenet)
#with data1, a genind object from Adegenet:

test <- genind2df(data1)
data2 <- convert_GC(test, 3, "/") 
#only if yours alleles are of length "3"

D. Description of data set

D.1 Discrimination of MLG

List unique alleles per locus:

Basic commands:

list_all_tab(zostera, vecpop = popvec)

or, for haploid data:

list_all_tab(haplodata, haploid = TRUE, vecpop = haplovec)

Results:

list_all_tab(zostera, vecpop = popvec)

#SaintMalo

knitr::kable(list_all_tab(zostera, vecpop = popvec)[[1]], align = "c")

#Arcouest

knitr::kable(list_all_tab(zostera, vecpop = popvec)[[2]], align = "c")

List MLG:

Basic commands:

MLG_tab(zostera, vecpop = popvec)

or, for haploid data:

MLG_tab(haplodata, vecpop = haplovec)

Results:

MLG_tab(zostera, vecpop = popvec)[[1]]
#SaintMalo

knitr::kable(MLG_tab(zostera, vecpop = popvec)[[1]][1:5,], align = "c")

Allelic frequencies:

Basic commands:

freq_RR(zostera, vecpop = popvec)

or, for haploid data:

freq_RR(haplodata, haploid = TRUE, vecpop = haplovec)

Options:

freq_RR(zostera, vecpop = popvec) #on ramets
freq_RR(zostera, vecpop = popvec, genet = TRUE) #on genets
freq_RR(zostera, vecpop = popvec, RR = TRUE) #Round-Robin methods

Results:

freq_RR(zostera, vecpop = popvec)[[1]]
#SaintMalo

res <- cbind(freq_RR(zostera, vecpop = popvec)[[1]], freq_RR(zostera, vecpop = popvec, genet = TRUE)[[1]][,3], freq_RR(zostera, vecpop = popvec, RR = TRUE)[[1]][,3])[1:7,]
colnames(res)[3:5] <- c("freq_ramet", "freq_genet", "freq_RR")
knitr::kable(res, align = "c")

D.2 Tests for reliability of loci and subsampling of individuals

On loci

Basic commands:

sample_loci(zostera, vecpop = popvec, nbrepeat = 1000)

or, for haploid data:

sample_loci(haplodata, haploid = TRUE, vecpop = haplovec, nbrepeat = 1000)

Options:

sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, He = TRUE) #with He results
sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, graph = TRUE) #graph displayed
sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, bar = TRUE) 
                                                #progression bar, could be time consuming
sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, export = TRUE) 
                                                #graph export in .eps

Results:

res <- sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, He = TRUE) 
names(res)

data(resvigncont2)
names(resvigncont2$res2_SU1)

names(res$SaintMalo)

names(resvigncont2$res2_SU1$SaintMalo)

#Results: MLG
res$Arcouest$res_MLG

knitr::kable(resvigncont2$res2_SU1$Arcouest$res_MLG, align = "c")

#Results: alleles
res$Arcouest$res_alleles

knitr::kable(res$Arcouest$res_alleles, align = "c")

#Results: raw data
#res$Arcouest$raw_He
#res$Arcouest$raw_MLG
#res$Arcouest$raw_all

boxplot(res$SaintMalo$raw_MLG, main = "Genotype accumulation curve",
    xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")

boxplot(resvigncont2$res2_SU1$SaintMalo$raw_MLG, main = "Genotype accumulation curve", xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")

boxplot(res$Arcouest$raw_MLG, main = "Genotype accumulation curve",
    xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")

boxplot(resvigncont2$res2_SU1$Arcouest$raw_MLG, main = "Genotype accumulation curve", xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")

Same on units

Basic commands:

sample_units(zostera, vecpop = popvec, nbrepeat = 1000)

or, for haploid data:

sample_units(haplodata, haploid = TRUE, vecpop = haplovec, nbrepeat = 1000)

This sub-sampling analysis deliver basic estimates of richness and diversity for an increasing number of sampling units.
They can be used to standardise estimates of populations with different sampling effort.

E. Discrimination of clonal lineages

E.1 psex/psex Fis with pvalue computation

pgen, psex and p-values

Basic commands:

pgen(zostera, vecpop = popvec)
psex(zostera, vecpop = popvec)

or, for haploid data:

pgen(haplodata, haploid = TRUE, vecpop = haplovec)
psex(haplodata, haploid = TRUE, vecpop = haplovec)

Options: (idem on psex and pgen)

#allelic frequencies computation:
psex(zostera, vecpop = popvec) #psex on ramets
psex(zostera, vecpop = popvec, genet = TRUE) #psex on genets
psex(zostera, vecpop = popvec, RR = TRUE) #psex with Round-Robin method
#psex computation
psex(zostera, vecpop = popvec) #psex with one psex per replica
psex(zostera, vecpop = popvec, MLGsim = TRUE) #psex MLGsim method
#pvalues:
psex(zostera, vecpop = popvec, nbrepeat = 100) #with p-values
psex(zostera, vecpop = popvec, nbrepeat = 1000, bar = TRUE) 
                                        #with p-values and a progression bar

Results:

res <- psex(zostera, vecpop = popvec, RR = TRUE, nbrepeat = 1000) 
res$Arcouest[[1]] 
#if nbrepeat != 0, res contains a table of psex values and a vector of sim-psex values

knitr::kable(resvigncont2$res2_PS2, align = "c")

res$Arcouest[[2]] #a part of sim-psex values

resvigncont2$res2_PS1$Arcouest[[2]][1:10]

Fis, pgen Fis, psex Fis and p-values

Not for haploid data !

Fis

Basic commands:

Fis(zostera, vecpop = popvec)

Options:

Fis(zostera, vecpop = popvec) #Fis on ramets
Fis(zostera, vecpop = popvec, genet = TRUE) #Fis on genets
Fis(zostera, vecpop = popvec, RR = TRUE) #Fis with Round-Robin methods
#RR = TRUE contains two results : a table with allelic frequencies 
                             #and a table with Fis results

Results:

Fis(zostera, vecpop = popvec, RR = TRUE)$Arcouest[[2]]

knitr::kable(Fis(zostera, vecpop = popvec, RR = TRUE)$Arcouest[[2]], align = "c")

pgen Fis, psex Fis and p-values

Basic commands: (idem for pgen_Fis and psex_Fis)

pgen_Fis(zostera, vecpop = popvec)

Options:

#allelic frequencies:
psex_Fis(zostera, vecpop = popvec) #psex Fis on ramets
psex_Fis(zostera, vecpop = popvec, genet = TRUE) #psex Fis on genets
psex_Fis(zostera, vecpop = popvec, RR = TRUE) #psex Fis with Round-Robin method
#psex computation
psex_Fis(zostera, vecpop = popvec) #psex Fis, one for each replica
psex_Fis(zostera, vecpop = popvec, MLGsim = TRUE) #psex Fis with MLGsim method
#pvalues
psex_Fis(zostera, vecpop = popvec, nbrepeat = 100) #with p-values
psex_Fis(zostera, vecpop = popvec, nbrepeat = 1000, bar = TRUE) 
                                            #with p-values and a progression bar

Results:

res <- psex_Fis(zostera, vecpop = popvec, RR = TRUE, nbrepeat = 1000) 
res$Arcouest[[1]] 
#if nbrepeat != 0, res contains a table of psex values and a vector of sim-psex Fis values

knitr::kable(resvigncont2$res2_PS4, align = "c")

res$Arcouest[[2]] #a part of sim psex Fis values

resvigncont2$res2_PS3$Arcouest[[2]][1:10]

E.2 Tests for MLLs occurrence and assessment of their memberships

Genetic distance matrix computation and threshold definition

On a theoretical diploid population with c = 0.9999 (c, clonality rate):

data(popsim)
vecsim <- c(rep(1,50), rep(2,50))

#genetic distances computation, distance on allele differences:
respop <- genet_dist(popsim, vecpop = vecsim)
ressim <- genet_dist_sim(popsim, vecpop = vecsim , nbrepeat = 1000) 
                                #theoretical distribution: sexual reproduction
ressimWS <- genet_dist_sim(popsim, vecpop = vecsim , genet = TRUE, nbrepeat = 1000) 
                                                            #idem, without selfing

respop <- resvigncont2$respop
ressim <- resvigncont2$ressim
ressimWS <- resvigncont2$ressimWS

#graph prep.:
#first pop: 
p1 <- hist(respop[[1]]$distance_matrix, freq = FALSE, col = rgb(0,0.4,1,1), 
            breaks = seq(0, max(respop[[1]]$distance_matrix)+1, 1), 
            main = "pop_1_sim", xlab = "")
p2 <- hist(ressim[[1]]$distance_matrix, freq = FALSE, col = rgb(0.7,0.9,1,0.5), 
            breaks = seq(0, max(ressim[[1]]$distance_matrix)+1, 1), 
            main = "pop_1_SR", xlab = "")
p3 <- hist(ressimWS[[1]]$distance_matrix, freq = FALSE, col = rgb(0.9,0.5,1,0.3), 
            breaks = seq(0, max(ressimWS[[1]]$distance_matrix)+1, 1), 
            main = "pop_1_SRWS", xlab = "")
limx <- max(max(respop[[1]]$distance_matrix), max(ressim[[1]]$distance_matrix), 
        max(ressimWS[[1]]$distance_matrix))

#graph superposition: 
plot(p1, col = rgb(0,0.4,1,1), freq = FALSE, xlim = c(0,limx), 
        main = paste("pop", unique(vecsim)[[1]], sep = "_"), 
        xlab = "Genetic distances")
plot(p2, col = rgb(0.7,0.9,1,0.5), freq = FALSE, add = TRUE)
plot(p3, col = rgb(0.9,0.5,1,0.3), freq = FALSE, add = TRUE)

#adding a legend:
leg.txt <- c("original data","simulated data", "without selfing")
col <- c(rgb(0,0.4,1,1), rgb(0.7,0.9,1,0.5), rgb(0.9,0.5,1,0.3))
legend("top", fill = col, leg.txt, plot = TRUE, bty = "o", box.lwd = 1.5, 
bg = "white")

#second pop:
p <- 2 #useful if several populations: just change *p* and run lines

p1 <- hist(respop[[p]]$distance_matrix, freq = FALSE, col = rgb(0,0.4,1,1), 
            breaks = seq(0, max(respop[[p]]$distance_matrix)+1, 1), 
            main = paste("pop", p, sep = "_"), xlab = "")
p2 <- hist(ressim[[p]]$distance_matrix, freq = FALSE, col = rgb(0.7,0.9,1,0.5), 
            breaks = seq(0, max(ressim[[p]]$distance_matrix)+1, 1), 
            main = paste("pop", p, sep = "_"), xlab = "")
p3 <- hist(ressimWS[[p]]$distance_matrix, freq = FALSE, col = rgb(0.9,0.5,1,0.3), 
            breaks = seq(0, max(ressimWS[[p]]$distance_matrix)+1, 1), 
            main = paste("pop", p, sep = "_"), xlab = "")
limx <- max(max(respop[[p]]$distance_matrix), max(ressim[[p]]$distance_matrix), 
            max(ressimWS[[p]]$distance_matrix))

#graph superposition: 
plot(p1, col = rgb(0,0.4,1,1), freq = FALSE, xlim = c(0,limx), 
        main = paste("pop", unique(vecsim)[[p]], sep = "_"), 
        xlab = "Genetic distances")
plot(p2, col = rgb(0.7,0.9,1,0.5), freq = FALSE, add = TRUE)
plot(p3, col = rgb(0.9,0.5,1,0.3), freq = FALSE, add = TRUE)

#adding a legend:
leg.txt <- c("original data","simulated data", "without selfing")
col <- c(rgb(0,0.4,1,1), rgb(0.7,0.9,1,0.5), rgb(0.9,0.5,1,0.3))
legend("top", fill = col, leg.txt, plot = TRUE, bty = "o", box.lwd = 1.5, 
bg = "white")

#determining alpha2
table(respop[[1]]$distance_matrix)
#alpha2 = 3

#creating MLL list:
MLLlist <- MLL_generator(popsim, vecpop = vecsim, alpha2 = c(3,0))
##This will create a list of MLL (alpha2 = 3) and MLG (alpha2 = 0) !

#or
res <- genet_dist(popsim, vecpop = vecsim, alpha2 = c(3,0))
MLLlist <- MLL_generator2(list(res[[1]]$potential_clones, 
    res[[2]]$potential_clones), MLG_list(popsim, vecpop = vecsim), vecpop = vecsim)

For haploid data, theoretical example:

respop <- genet_dist(haplodata, haploid = TRUE, vecpop = vechaplo)
ressim <- genet_dist_sim(haplodata, haploid = TRUE, vecpop = vechaplo, 
                            nbrepeat = 1000)
MLLlist <- MLL_generator(haplodata, haploid = TRUE, vecpop = vechaplo, 
                            alpha2 = c(3,0))
#or
res <- genet_dist(haplodata, haploid = TRUE, vecpop = vechaplo, alpha2 = c(3,0))
MLLlist <- MLL_generator2(list(res[[1]]$potential_clones, res[[2]]$potential_clones), 
            haploid = TRUE, MLG_list(haplodata, vecpop = vechaplo), vecpop = vechaplo)

F. Genotypic diversity, richness and evenness indices calculation

F.1 Classic genotypic indices

Basic commands:

clonal_index(zostera, vecpop = popvec)

or, with MLL:

clonal_index(popsim, vecpop = vecsim, listMLL = MLLlist)

or, for haploid data:

clonal_index(haplodata, vecpop = vechaplo)

Results:

clonal_index(zostera, vecpop = popvec)

knitr::kable(resvigncont2$res2_ci, align = "c")

F.2 Pareto index

Basic commands:

Pareto_index(zostera, vecpop = popvec)

or, with MLL:

Pareto_index(popsim, vecpop = vecsim, listMLL = MLLlist)

or, for haploid data:

Pareto_index(haplodata, vecpop = vechaplo)

Options:

Pareto_index(zostera, vecpop = popvec, graph = TRUE) #classic graphic
Pareto_index(zostera, vecpop = popvec, legends = 2, export = TRUE) 
                                                        #export option
Pareto_index(zostera, vecpop = popvec, full = TRUE) #all results

Results:

res <- Pareto_index(zostera, vecpop = popvec, full = TRUE, graph = TRUE, legends = 2)

require(RClone)
resz <- split(zostera, popvec)
res1 <- Pareto_index(resz[[1]],  full = TRUE, graph = TRUE, legends = 2)
res2 <- Pareto_index(resz[[2]],  full = TRUE, graph = TRUE, legends = 2)

names(res$SaintMalo)

names(res1)

res$SaintMalo$Pareto

res1$Pareto

res$SaintMalo$c_Pareto

res1$c_Pareto

#res$SaintMalo$regression_results
#res$SaintMalo$coords_Pareto #points coordinates

G. Spatial components of clonality

G.1 Spatial autocorrelation

Basic commands:

autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE)

or, with MLL:

autocorrelation(popsim, coords = coord_sim, Loiselle = TRUE, listMLL = MLLlist)

or, for haploid data:

autocorrelation(haplodata, haploid = TRUE, coords = coord_haplo, Loiselle = TRUE)

Lot's of options:

#kinship distances:
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE)
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Ritland = TRUE)

#ramets/genets methods:
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE) 
                                                                            #ramets
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec,, Loiselle = TRUE, 
                    genet = TRUE, central_coords = TRUE) 
                                            #genets, central coordinates of each MLG
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE, 
                    genet = TRUE, random_unit = TRUE) 
                                                    #genets, one random unit per MLG
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE, 
                    genet = TRUE, weighted = TRUE) 
                                            #genets, with weighted matrix on kinships

#distance classes construction:
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE) 
                                                                #10 equidistant classes
distvec <- c(0,10,15,20,30,50,70,76.0411074) 
                                    #with 0, min distance and 76.0411074, max distance
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE, 
                    vecdist = distvec) #custom distance vector
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE, 
                    class1 = TRUE, d = 7) #7 equidistant classes
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE, 
                    class2 = TRUE, d = 7) 
                            #7 distance classes with the same number of units in each

#graph options:
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Ritland = TRUE, 
                                                        graph = TRUE) #displays graph
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Ritland = TRUE, 
                                                        export = TRUE) #export graph

#pvalues computation
autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Ritland = TRUE, 
                                                                    nbrepeat = 1000)

Results:

res <- autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, 
                                        Ritland = TRUE, nbrepeat = 1000, graph = TRUE)

plot(resvigncont2$res2auto1$Main_results[,3], resvigncont2$res2auto1$Main_results[,6], main = "Spatial aucorrelation analysis",
ylim = c(-0.2,0.2), type = "l", xlab = "Spatial distance", ylab = "Coancestry (Fij)")
points(resvigncont2$res2auto1$Main_results[,3], resvigncont2$res2auto1$Main_results[,6], pch = 20)
abline(h = 0, lty = 3)

plot(resvigncont2$res2auto2$Main_results[,3], resvigncont2$res2auto2$Main_results[,6], main = "Spatial aucorrelation analysis",
ylim = c(-0.2,0.2), type = "l", xlab = "Spatial distance", ylab = "Coancestry (Fij)")
points(resvigncont2$res2auto2$Main_results[,3], resvigncont2$res2auto2$Main_results[,6], pch = 20)
abline(h = 0, lty = 3)

names(res$Arcouest)

names(resvigncont2$res2auto2)

res$Arcouest$Main_results #enables graph reproduction

knitr::kable(resvigncont2$res2auto2$Main_results, align = "c")

apply(res$Arcouest$Main_results, 2, mean)[6] #mean Fij

apply(resvigncont2$res2auto2$Main_results, 2, mean)[6]

res$Arcouest$Slope_and_Sp_index #gives b and Sp indices

knitr::kable(resvigncont2$res2auto2$Slope_and_Sp_index, align = "c")

#raw data:
#res$Arcouest$Slope_resample
#res$Arcouest$Kinship_resample
#res$Arcouest$Matrix_kinship_results
#res$Arcouest$Class_kinship_results 
#res$Arcouest$Class_distance_results

G.2 Clonal subrange

Basic commands:

clonal_sub(zostera, coords = coord_zostera, vecpop = popvec)

or, with MLL:

clonal_sub(popsim, coords = coord_sim, listMLL = MLLlist)

or, for haploid data:

clonal_sub(haplodata, haploid = TRUE, coords = coord_haplo)

Options: same distance classes definition as autocorrelation:

clonal_sub(posidonia, coords = coord_posidonia) #basic, with 10 equidistant classes
distvec <- c(0,10,15,20,30,50,70,76.0411074) 
                                #with 0, min distance and 76.0411074, max distance
clonal_sub(zostera, coords = coord_zostera, vecpop = popvec, vecdist = distvec) 
                                                            #custom distance classes
clonal_sub(zostera, coords = coord_zostera, vecpop = popvec, class1 = TRUE, d = 7) 
                                                                #7 equidistant classes
clonal_sub(zostera, coords = coord_zostera, vecpop = popvec, class1 = TRUE, d = 7) 
                            #7 distance classes with the same number of units in each

Results:

res <- clonal_sub(zostera, coords = coord_zostera, vecpop = popvec)
res$Arcouest[[1]] #Global clonal subrange

res$Arcouest$clonal_sub_tab  #details per class

knitr::kable(res$Arcouest$clonal_sub_tab, align ="c")

G.3 Aggregation index

Basic commands:

agg_index(zostera, coords = coord_zostera, vecpop = popvec)

or, with MLL:

agg_index(popsim, coords = coord_sim, listMLL = MLLlist)

or, for haploid data:

agg_index(haplodata, coords = coord_haplo)

Options:

agg_index(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 100) 
                                                            #pvalue computation
agg_index(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 1000, 
                                            bar = TRUE) #could be time consuming

Results:

res <- agg_index(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 1000)

res$SaintMalo$results #Aggregation index

knitr::kable(resvigncont2$res2_agg$SaintMalo$results, align = "c")

#res$SaintMalo$simulation #vector of sim aggregation index

G.4 Edge Effect

Basic commands:

#for zostera, centers of quadra is at 15,10
edge_effect(zostera, coords = coord_zostera, vecpop = popvec, 
                center = rep(c(15,10),2))

or, with MLL:

edge_effect(popsim, coords = coord_sim, center = rep(c(15,10),2), listMLL = MLLlist)

or, for haploid data:

edge_effect(haplodata, coords = coord_haplo, center = rep(c(15,10),2))

Options:

edge_effect(zostera, coords = coord_zostera, vecpop = popvec, center = rep(c(15,10),2), 
                                                    nbrepeat = 100) #pvalue computation
edge_effect(zostera, coords = coord_zostera, vecpop = popvec, center = rep(c(15,10),2), 
                                    nbrepeat = 1000, bar = TRUE) #could be time consuming

Results:

res <- edge_effect(zostera, coords = coord_zostera, vecpop = popvec, 
        center = rep(c(15,10),2), nbrepeat = 100) #better put 1000 nbrepeat at least

res$SaintMalo$results #Aggregation index

knitr::kable(resvigncont2$res2_ee$SaintMalo$results, align = "c")

#res$SaintMalo$simulation #vector of sim aggregation index

H. BONUS: "Ready to use" Table

Summary function of main results:

Basic commands:

GenClone(zostera, coords = coord_zostera, vecpop = popvec)

or, with MLL:

GenClone(popsim, coords = coord_sim, listMLL = MLLlist)

or, for haploid data:

GenClone(haplodata, haploid = TRUE, coords = coord_haplo)

Options:

GenClone(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 100) #pvalues
GenClone(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 1000, bar = TRUE) 
                                                                #could be time consuming

Results:

GenClone(zostera, coords = coord_zostera, vecpop = popvec)

knitr::kable(resvigncont2$res2_gen[,1:9], longtable = TRUE, align = "c")
knitr::kable(resvigncont2$res2_gen[,10:16], longtable = TRUE, align = "c")
knitr::kable(resvigncont2$res2_gen[,17:24], longtable = TRUE, align = "c")

When a locus is homozygous, it is ignored for Fis and Fis_WR values computation.

Any scripts or data that you put into this service are public.

RClone documentation built on May 15, 2021, 5:07 p.m.

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Note that we can't provide technical support on individual packages. You should contact the package authors for that.

RClone
Partially Clonal Populations Analysis

RClone quickmanual: several populations
In RClone: Partially Clonal Populations Analysis

"Eager Beginners" Manual for RClone package

A. Introduction to RClone

B. RClone data format: several population

C. General format

D. Description of data set

D.1 Discrimination of MLG

D.2 Tests for reliability of loci and subsampling of individuals

E. Discrimination of clonal lineages

E.1 psex/psex Fis with pvalue computation

E.2 Tests for MLLs occurrence and assessment of their memberships

F. Genotypic diversity, richness and evenness indices calculation

F.1 Classic genotypic indices

F.2 Pareto index

G. Spatial components of clonality

G.1 Spatial autocorrelation

G.2 Clonal subrange

G.3 Aggregation index

G.4 Edge Effect

H. BONUS: "Ready to use" Table

Try the RClone package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

RClone Partially Clonal Populations Analysis

RClone quickmanual: several populations In RClone: Partially Clonal Populations Analysis

"Eager Beginners" Manual for RClone package

A. Introduction to RClone

B. RClone data format: several population

C. General format

D. Description of data set

D.1 Discrimination of MLG

D.2 Tests for reliability of loci and subsampling of individuals

E. Discrimination of clonal lineages

E.1 psex/psex Fis with pvalue computation

E.2 Tests for MLLs occurrence and assessment of their memberships

F. Genotypic diversity, richness and evenness indices calculation

F.1 Classic genotypic indices

F.2 Pareto index

G. Spatial components of clonality

G.1 Spatial autocorrelation

G.2 Clonal subrange

G.3 Aggregation index

G.4 Edge Effect

H. BONUS: "Ready to use" Table

Try the RClone package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

RClone
Partially Clonal Populations Analysis

RClone quickmanual: several populations
In RClone: Partially Clonal Populations Analysis