## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = ">", dev = 'pdf')
## ------------------------------------------------------------------------
library(RClone)
data(posidonia)
## ---- echo = FALSE, results = 'asis'-------------------------------------
knitr::kable(posidonia[1:10,1:8], align = "c")
## ---- eval = FALSE-------------------------------------------------------
# data(posidonia)
#
# sort_all(posidonia)
## ------------------------------------------------------------------------
#Let's create your example table:
test <- matrix("232/231", ncol = 2, nrow = 2)
colnames(test) <- paste("locus", 1:2, sep = "_")
#Use :
data1 <- convert_GC(as.data.frame(test), 3, "/")
## ---- eval = FALSE-------------------------------------------------------
# data1
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(data1, align = "c")
## ---- eval = FALSE-------------------------------------------------------
# #library(adegenet)
# #with data1, a genind object from Adegenet:
#
# test <- genind2df(data1)
# data2 <- convert_GC(test, 3, "/")
# #only if yours alleles are of length "3"
## ---- eval = FALSE-------------------------------------------------------
# data(infile)
#
# #This is nearly a GenClone file, type:
# write.table(infile, "infile.csv", col.names = FALSE, row.names = FALSE, sep = ";")
#
# #Now you have a formatted GenClone file:
# res <- transcript_GC("infile.csv", ";", 2, 7, 3)
# posidonia <- res$data_genet
# coord_posidonia <- res$data_coord
## ---- eval = FALSE-------------------------------------------------------
# data(posidonia)
#
# list_all_tab(posidonia)
## ---- eval = FALSE-------------------------------------------------------
# list_all_tab(haplodata, haploid = TRUE)
## ---- eval = FALSE-------------------------------------------------------
# list_all_tab(posidonia)
## ---- echo = FALSE-------------------------------------------------------
data(posidonia)
knitr::kable(list_all_tab(posidonia), align = "c")
## ---- eval = FALSE-------------------------------------------------------
# MLG_tab(posidonia)
## ---- eval = FALSE-------------------------------------------------------
# MLG_tab(haplodata)
## ---- eval = FALSE-------------------------------------------------------
# MLG_tab(posidonia)
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(MLG_tab(posidonia)[1:5,], align = "c")
## ---- eval = FALSE-------------------------------------------------------
# freq_RR(posidonia)
## ---- eval = FALSE-------------------------------------------------------
# freq_RR(haplodata, haploid = TRUE)
## ---- eval = FALSE-------------------------------------------------------
# freq_RR(posidonia) #on ramets
# freq_RR(posidonia, genet = TRUE) #on genets
# freq_RR(posidonia, RR = TRUE) #Round-Robin methods
## ---- eval = FALSE-------------------------------------------------------
# freq_RR(posidonia)
## ---- echo = FALSE-------------------------------------------------------
res <- cbind(freq_RR(posidonia), freq_RR(posidonia, genet = TRUE)[,3], freq_RR(posidonia, RR = TRUE)[,3])[1:7,]
colnames(res)[3:5] <- c("freq_ramet", "freq_genet", "freq_RR")
knitr::kable(res, align = "c")
## ---- eval = FALSE-------------------------------------------------------
# sample_loci(posidonia, nbrepeat = 1000)
## ---- eval = FALSE-------------------------------------------------------
# sample_loci(haplodata, haploid = TRUE, nbrepeat = 1000)
## ---- eval = FALSE-------------------------------------------------------
# sample_loci(posidonia, nbrepeat = 1000, He = TRUE) #with He results
# sample_loci(posidonia, nbrepeat = 1000, graph = TRUE) #graph displayed
# sample_loci(posidonia, nbrepeat = 1000, bar = TRUE) #progression bar
# #could be time consuming
# sample_loci(posidonia, nbrepeat = 1000, export = TRUE) #graph export in .eps
## ---- eval = FALSE-------------------------------------------------------
# res <- sample_loci(posidonia, nbrepeat = 1000, He = TRUE) #time consuming
# names(res)
## ---- echo = FALSE-------------------------------------------------------
data(resvigncont)
names(resvigncont$resvigncont$res_SU1)
## ---- eval = FALSE-------------------------------------------------------
# #Results: MLG
# res$res_MLG
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$res_SU1$res_MLG, align = "c")
## ---- eval = FALSE-------------------------------------------------------
# #Results: alleles
# res$res_alleles
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$res_SU1$res_alleles, align = "c")
## ------------------------------------------------------------------------
#Results: raw data
#res$raw_He
#res$raw_MLG
#res$raw_all
## ---- eval = FALSE-------------------------------------------------------
# boxplot(res$raw_MLG, main = "Genotype accumulation curve",
# xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")
## ---- echo = FALSE-------------------------------------------------------
boxplot(resvigncont$res_SU1$raw_MLG, main = "Genotype accumulation curve", xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")
## ---- eval = FALSE-------------------------------------------------------
# sample_units(posidonia, nbrepeat = 1000)
## ---- eval = FALSE-------------------------------------------------------
# sample_units(haplodata, haploid = TRUE, nbrepeat = 1000)
## ---- eval = FALSE-------------------------------------------------------
# pgen(posidonia)
# psex(posidonia)
## ---- eval = FALSE-------------------------------------------------------
# pgen(haplodata, haploid = TRUE)
# psex(haplodata, haploid = TRUE)
## ---- eval = FALSE-------------------------------------------------------
# #allelic frequencies computation:
# psex(posidonia) #psex on ramets
# psex(posidonia, genet = TRUE) #psex on genets
# psex(posidonia, RR = TRUE) #psex with Round-Robin method
# #psex computation
# psex(posidonia) #psex with one psex per replica
# psex(posidonia, MLGsim = TRUE) #psex MLGsim method
# #pvalues:
# psex(posidonia, nbrepeat = 100) #with p-values
# psex(posidonia, nbrepeat = 1000, bar = TRUE) #with p-values and a progression bar
## ---- eval = FALSE-------------------------------------------------------
# res <- psex(posidonia, RR = TRUE, nbrepeat = 1000)
# res[[1]] #if nbrepeat != 0, res contains a table of psex values
# #and a vector of sim-psex values
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$res_PS2, align = "c")
## ---- eval = FALSE-------------------------------------------------------
# res[[2]] #sim psex values
## ---- echo = FALSE-------------------------------------------------------
resvigncont$res_PS1[[2]]
## ---- eval = FALSE-------------------------------------------------------
# Fis(posidonia)
## ---- eval = FALSE-------------------------------------------------------
# Fis(posidonia) #Fis on ramets
# Fis(posidonia, genet = TRUE) #Fis on genets
# Fis(posidonia, RR = TRUE) #Fis with Round-Robin methods
# #RR = TRUE contains two results : a table with allelic frequencies
# #and a table with Fis results
## ---- eval = FALSE-------------------------------------------------------
# Fis(posidonia, RR = TRUE)[[2]]
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(Fis(posidonia, RR = TRUE)[[2]], align = "c")
## ---- eval = FALSE-------------------------------------------------------
# pgen_Fis(posidonia)
## ---- eval = FALSE-------------------------------------------------------
# #allelic frequencies:
# psex_Fis(posidonia) #psex Fis on ramets
# psex_Fis(posidonia, genet = TRUE) #psex Fis on genets
# psex_Fis(posidonia, RR = TRUE) #psex Fis with Round-Robin method
# #psex computation
# psex_Fis(posidonia) #psex Fis, one for each replica
# psex_Fis(posidonia, MLGsim = TRUE) #psex Fis with MLGsim method
# #pvalues
# psex_Fis(posidonia, nbrepeat = 100) #with p-values
# psex_Fis(posidonia, nbrepeat = 1000, bar = TRUE) #with p-values and a progression bar
## ---- eval = FALSE-------------------------------------------------------
# res <- psex_Fis(posidonia, RR = TRUE, nbrepeat = 1000)
# res[[1]]
# #if nbrepeat != 0, res contains a table of psex values
# #and a vector of sim-psex Fis values
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$res_PS4, align = "c")
## ---- eval = FALSE-------------------------------------------------------
# res[[2]] #sim psex Fis values
## ---- echo = FALSE-------------------------------------------------------
resvigncont$res_PS3[[2]]
## ---- eval = FALSE-------------------------------------------------------
# data(popsim)
#
# #genetic distances computation, distance on allele differences:
# respop <- genet_dist(popsim)
# ressim <- genet_dist_sim(popsim, nbrepeat = 1000) #theoretical distribution:
# #sexual reproduction
# ressimWS <- genet_dist_sim(popsim, genet = TRUE, nbrepeat = 1000) #idem, without selfing
## ---- echo = FALSE-------------------------------------------------------
data(popsim)
respop <- resvigncont$respop
ressim <- resvigncont$ressim
ressimWS <- resvigncont$ressimWS
## ---- fig.width = 10, fig.height = 8-------------------------------------
#graph prep.:
p1 <- hist(respop$distance_matrix, freq = FALSE, col = rgb(0,0.4,1,1), main = "popsim",
xlab = "Genetic distances", breaks = seq(0, max(respop$distance_matrix)+1, 1))
p2 <- hist(ressim$distance_matrix, freq = FALSE, col = rgb(0.7,0.9,1,0.5), main = "popSR",
xlab = "Genetic distances", breaks = seq(0, max(ressim$distance_matrix)+1, 1))
p3 <- hist(ressimWS$distance_matrix, freq = FALSE, col = rgb(0.9,0.5,1,0.3),
main = "popSRWS", xlab = "Genetic distances",
breaks = seq(0, max(ressimWS$distance_matrix)+1, 1))
limx <- max(max(respop$distance_matrix), max(ressim$distance_matrix),
max(ressimWS$distance_matrix))
#graph superposition:
plot(p1, col = rgb(0,0.4,1,1), freq = FALSE, xlim = c(0,limx), main = "",
xlab = "Genetic distances")
plot(p2, col = rgb(0.7,0.9,1,0.5), freq = FALSE, add = TRUE)
plot(p3, col = rgb(0.9,0.5,1,0.3), freq = FALSE, add = TRUE)
#adding a legend:
leg.txt <- c("original data","simulated data", "without selfing")
col <- c(rgb(0,0.4,1,1), rgb(0.7,0.9,1,0.5), rgb(0.9,0.5,1,0.3))
legend("top", fill = col, leg.txt, plot = TRUE, bty = "o", box.lwd = 1.5,
bg = "white")
## ------------------------------------------------------------------------
#determining alpha2
table(respop$distance_matrix)
#alpha2 = 4
## ------------------------------------------------------------------------
#creating MLL list:
MLLlist <- MLL_generator(popsim, alpha2 = 4)
#or
res <- genet_dist(popsim, alpha2 = 4)
MLLlist <- MLL_generator2(res$potential_clones, MLG_list(popsim))
## ---- eval = FALSE-------------------------------------------------------
# respop <- genet_dist(haplodata, haploid = TRUE)
# ressim <- genet_dist_sim(haplodata, haploid = TRUE, nbrepeat = 1000)
# MLLlist <- MLL_generator(haplodata, haploid = TRUE, alpha2 = 4)
# #or
# res <- genet_dist(haplodata, haploid = TRUE, alpha2 = 4)
# MLLlist <- MLL_generator2(res$potential_clones, haploid = TRUE, MLG_list(haplodata))
## ---- eval = FALSE-------------------------------------------------------
# clonal_index(posidonia)
## ---- eval = FALSE-------------------------------------------------------
# clonal_index(popsim, listMLL = MLLlist)
## ---- eval = FALSE-------------------------------------------------------
# clonal_index(haplodata)
## ---- eval = FALSE-------------------------------------------------------
# clonal_index(posidonia)
## ---- echo = FALSE, results = 'asis'-------------------------------------
knitr::kable(resvigncont$rescl, align = "c")
data(coord_posidonia)
## ---- eval = FALSE-------------------------------------------------------
# Pareto_index(posidonia)
## ---- eval = FALSE-------------------------------------------------------
# Pareto_index(popsim, listMLL = MLLlist)
## ---- eval = FALSE-------------------------------------------------------
# Pareto_index(haplodata)
## ---- eval = FALSE-------------------------------------------------------
# Pareto_index(posidonia, graph = TRUE) #classic graphic
# Pareto_index(posidonia, legends = 2, export = TRUE) #export option
# Pareto_index(posidonia, full = TRUE) #all results
## ------------------------------------------------------------------------
res <- Pareto_index(posidonia, full = TRUE, graph = TRUE, legends = 2)
names(res)
res$Pareto
res$c_Pareto
#res$regression_results
#res$coords_Pareto #points coordinates
## ---- eval = FALSE-------------------------------------------------------
# autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE)
## ---- eval = FALSE-------------------------------------------------------
# autocorrelation(popsim, coords = coord_sim, Loiselle = TRUE, listMLL = MLLlist)
## ---- eval = FALSE-------------------------------------------------------
# autocorrelation(haplodata, haploid = TRUE, coords = coord_haplo, Loiselle = TRUE)
## ---- eval = FALSE-------------------------------------------------------
# data(posidonia)
# data(coord_posidonia)
#
# #kinship distances:
# autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE)
# autocorrelation(posidonia, coords = coord_posidonia, Ritland = TRUE)
#
# #ramets/genets methods:
# autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE) #ramets
# autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE,
# genet = TRUE, central_coords = TRUE)
# #genets, central coordinates of each MLG
# autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE,
# genet = TRUE, random_unit = TRUE) #genets, one random unit per MLG
# autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE,
# genet = TRUE, weighted = TRUE) #genets, with weighted matrix on kinships
#
# #distance classes construction:
# autocorrelation(posidonia, coords = coord_posidonia, 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(posidonia, coords = coord_posidonia, Loiselle = TRUE,
# vecdist = distvec) #custom distance vector
# autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE,
# class1 = TRUE, d = 7) #7 equidistant classes
# autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE,
# class2 = TRUE, d = 7)
# #7 distance classes with the same number of units in each
#
# #graph options:
# autocorrelation(posidonia, coords = coord_posidonia, Ritland = TRUE, graph = TRUE)
# #displays graph
# autocorrelation(posidonia, coords = coord_posidonia, Ritland = TRUE, export = TRUE)
# #export graph
#
# #pvalues computation
# autocorrelation(posidonia, coords = coord_posidonia, Ritland = TRUE, nbrepeat = 1000)
## ---- eval = FALSE-------------------------------------------------------
# res <- autocorrelation(posidonia, coords = coord_posidonia, Ritland = TRUE,
# nbrepeat = 1000, graph = TRUE)
## ---- echo = FALSE-------------------------------------------------------
plot(resvigncont$resauto$Main_results[,3], resvigncont$resauto$Main_results[,6], main = "Spatial aucorrelation analysis",
ylim = c(-0.2,0.2), type = "l", xlab = "Spatial distance", ylab = "Coancestry (Fij)")
points(resvigncont$resauto$Main_results[,3], resvigncont$resauto$Main_results[,6], pch = 20)
abline(h = 0, lty = 3)
## ---- eval = FALSE-------------------------------------------------------
# names(res)
## ---- echo = FALSE-------------------------------------------------------
names(resvigncont$resauto)
## ---- eval = FALSE-------------------------------------------------------
# res$Main_results #enables graph reproduction
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$resauto$Main_results, align = "c")
## ---- eval = FALSE-------------------------------------------------------
# apply(res$Main_results, 2, mean)[6] #mean Fij
## ---- echo = FALSE-------------------------------------------------------
apply(resvigncont$resauto$Main_results, 2, mean)[6] #mean Fij
## ---- eval = FALSE-------------------------------------------------------
# res$Slope_and_Sp_index #gives b and Sp indices
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$resauto$Slope_and_Sp_index, align = "c")
## ------------------------------------------------------------------------
#raw data:
#res$Slope_resample
#res$Kinship_resample
#res$Matrix_kinship_results
#res$Class_kinship_results
#res$Class_distance_results
## ---- eval = FALSE-------------------------------------------------------
# clonal_sub(posidonia, coords = coord_posidonia)
## ---- eval = FALSE-------------------------------------------------------
# clonal_sub(popsim, coords = coord_sim, listMLL = MLLlist)
## ---- eval = FALSE-------------------------------------------------------
# clonal_sub(haplodata, haploid = TRUE, coords = coord_haplo)
## ---- eval = FALSE-------------------------------------------------------
# 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(posidonia, coords = coord_posidonia, vecdist = distvec)
# #custom distance classes
# clonal_sub(posidonia, coords = coord_posidonia, class1 = TRUE, d = 7)
# #7 equidistant classes
# clonal_sub(posidonia, coords = coord_posidonia, class1 = TRUE, d = 7)
# #7 distance classes with the same number of units in each
## ---- eval = FALSE-------------------------------------------------------
# res <- clonal_sub(posidonia, coords = coord_posidonia)
# res[[1]] #Global clonal subrange
## ---- echo = FALSE-------------------------------------------------------
resvigncont$rescs[[1]]
## ---- eval = FALSE-------------------------------------------------------
# res$clonal_sub_tab #details per class
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$rescs$clonal_sub_tab, align ="c")
## ---- eval = FALSE-------------------------------------------------------
# agg_index(posidonia, coords = coord_posidonia)
## ---- eval = FALSE-------------------------------------------------------
# agg_index(popsim, coords = coord_sim, listMLL = MLLlist)
## ---- eval = FALSE-------------------------------------------------------
# agg_index(haplodata, coords = coord_haplo)
## ---- eval = FALSE-------------------------------------------------------
# agg_index(posidonia, coords = coord_posidonia, nbrepeat = 100) #pvalue computation
# agg_index(posidonia, coords = coord_posidonia, nbrepeat = 1000, bar = TRUE)
# #could be time consuming
## ---- eval = FALSE-------------------------------------------------------
# res <- agg_index(posidonia, coords = coord_posidonia, nbrepeat = 1000)
## ---- eval = FALSE-------------------------------------------------------
# res$results #Aggregation index
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$resagg$results, align = "c")
## ------------------------------------------------------------------------
#res$simulation #vector of sim aggregation index
## ---- eval = FALSE-------------------------------------------------------
# #for posidonia, center of quadra is at 40,10
# edge_effect(posidonia, coords = coord_posidonia, center = c(40,10))
## ---- eval = FALSE-------------------------------------------------------
# edge_effect(popsim, coords = coord_sim, center = c(40,10), listMLL = MLLlist)
## ---- eval = FALSE-------------------------------------------------------
# edge_effect(haplodata, coords = coord_haplo, center = c(40,10))
## ---- eval = FALSE-------------------------------------------------------
# edge_effect(posidonia, coords = coord_posidonia, center = c(40,10), nbrepeat = 100)
# #pvalue computation
# edge_effect(posidonia, coords = coord_posidonia, center = c(40,10), nbrepeat = 1000,
# bar = TRUE) #could be time consuming
## ---- eval = FALSE-------------------------------------------------------
# res <- edge_effect(posidonia, coords = coord_posidonia, center = c(40,10), nbrepeat = 1000)
## ---- eval = FALSE-------------------------------------------------------
# res$results #Aggregation index
## ---- echo = FALSE-------------------------------------------------------
knitr::kable(resvigncont$resee$results, align = "c")
## ------------------------------------------------------------------------
#res$simulation #vector of sim aggregation index
## ---- eval = FALSE-------------------------------------------------------
# genclone(posidonia, coords = coord_posidonia)
## ---- eval = FALSE-------------------------------------------------------
# genclone(popsim, coords = coord_sim, listMLL = MLLlist)
## ---- eval = FALSE-------------------------------------------------------
# genclone(haplodata, haploid = TRUE, coords = coord_haplo)
## ---- eval = FALSE-------------------------------------------------------
# GenClone(posidonia, coords = coord_posidonia, nbrepeat = 100) #pvalues
# GenClone(posidonia, coords = coord_posidonia, nbrepeat = 1000, bar = TRUE)
# #could be time consuming
## ---- eval = FALSE-------------------------------------------------------
# GenClone(posidonia, coords = coord_posidonia)
## ---- echo = FALSE, results = 'asis'-------------------------------------
knitr::kable(resvigncont$resgen[,1:10], longtable = TRUE, align = "c")
knitr::kable(resvigncont$resgen[,11:17], longtable = TRUE, align = "c")
knitr::kable(resvigncont$resgen[,18:24], longtable = TRUE, align = "c")
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