test delete

test.txt

-test depot gitlab 1
-ligne 1
-ligne 2 

test2.txt

-Fichier 2 pour test depot gitlab

v1_0126.R

-############################
-## Population genetics : general script
-## J. Plantade
-## Jan 2021 
-############################
-
-# packages
-library(ade4)
-library(pegas)
-library(adegenet)
-library(PopGenome)
-library(hierfstat)
-library(ggplot2)
-library(poppr)
-
-################
-### setting data
-
-# data import into a loci object
-loci <- read.vcf("data/CD4_panTro3.vcf_fixed.vcf", from = 1, to = 572)
-nloci <- length(loci)
-# convert loci object into a genind object
-genind <- loci2genind(loci)
-# add population factor
-pop(genind)<-rep(c("paniscus","troglodytes_ellioti",
-                   "troglodytes_schweinfurthii","troglodytes_troglodytes","troglodytes_verus"), 
-                 c(11,10,6,4, 5))
-# convert genind object into a hierfstat object
-hierf <-  genind2hierfstat(genind)
-
-##################
-### heterozygosity
-
-div <- summary(genind)
-locinfo<-VCFloci("data/CD4_panTro3.vcf_fixed.vcf", what = "all", chunk.size = 1e9, quiet = FALSE)
-# ggplot2 : Hobs-Hexp VS position
-g1 <- ggplot() + geom_point(aes(x=locinfo$POS, y=div$Hobs-div$Hexp), size=0.3) + xlab("Position") + ylab("Hobs - Hexp") + ggtitle("Difference between Hobs and Hexp for each position") + theme_light()
-g1
-# Bartlett test (homogeneity of variance)
-# H0 : Hobs = Hexp
-bartlett.test(list(div$Hexp, div$Hobs)) # S : population is structured
-# create one genind object for each population
-pop <- c("pan", "ellioti", "schwein", "troglo", "verus")
-npop <- length(pop)
-for (i in 1:length(pop)){
-  tmp <- popsub(genind, sublist = c(subspecies[i]))
-  assign(pop[i], tmp)
-}
-# extracting heterozygosity for each population
-list_genind <- c(pan, ellioti, schwein, troglo, verus)
-for (i in 1:length(list_genind)){
-  tmp <- summary(list_genind[[i]])
-  assign(paste0(pop[i], "_div"), tmp)
-}
-# -> possibility to to Bartlett test and plot Hobs for each pop
-
-#######
-### Fst
-
-# Matrix X with colnames and rownames = pop, create the name of pairs
-npop <- 5
-M <- matrix(NA, nrow = npop, ncol = npop)
-M <- as.data.frame(M)            
-rownames(M) <- pop            
-colnames(M) <- pop
-for (i in 1:npop){
-  start <- colnames(M)[i]
-  for (j in i+1:npop-i){
-    end <- rownames(M)[j]
-    M[j, i] <- paste0(start, "_", end)
-  }
-}
-# creates a list of pair names
-pair_name <- c(rep("NA", (npop^2-npop)/2))
-k <- 1
-for (i in 1:(npop-1)){
-  for (j in i+1:(npop-i)){
-    pair_name[k] <- M[j, i]
-    k <- k+1
-  }
-}
-
-# genind subset -> couple of population.
-pair_list <- NULL
-for (i in 2:(npop)){ # in matrix M we go from col 2 to 5
-  for (j in (i+1):(npop+1)){ # in matrix M we go from row 3 to 6
-    tmp <- popsub(genind, sublist = c(pop(genind)[i-1], pop(genind)[j-1]))
-    tmp2 <- assign(M[j, i], tmp)
-    pair_list <- c(pair_list, tmp2) # list of all pop pairs
-  }
-}
-
-# calculation of F statistics and extraction of Fst values
-fst <- matrix(NA, nrow = nloci, ncol = length(pair_list))
-for (i in 1:length(pair_list)){
-  tmp <- Fst(as.loci(pair_list[[i]]), na.alleles = "")
-  fst[,i] <- tmp[,2]
-} 
-fst <- as.data.frame(fst)
-colnames(fst) <- pair_name
-
-# Fst values VS loci position
-plot(locinfo$POS, fst[,1], cex=0.2, xlab="Position", ylab="Fst value", main="Fst value per locus")
-abline(0.15, 0)
-
-#############
-## Clustering with DAPC 
-
-# Identify the number of cluster
-grp <- find.clusters(genind, max.n.clust = 10) #PCs to retain = 35; nb of clusters = 4
-
-# Kstat : BIC for each value of k, here k=4 has the lower BIC
-grp$Kstat
-# stat : gives the selected nb of clusters (selected k) and its BIC value
-grp$stat
-# grp : gives the id of the group to which each individual is assigned
-head(grp$grp)
-# size : number of individuals assigned to each group
-grp$size
-
-# Compare inferred groups (inf) with actual groups (ori)
-# Find actual groups with function pop on the genind object
-pop(genind)
-tab <- table(pop(genind), grp$grp)
-# Display
-table.value(tab, col.labels = colnames(tab), row.labels = rownames(tab))
-
-# describe clusters using dapc1
-dapc1 <- dapc(genind, grp$grp) # PCs=10, Linear discriminant=5
-# visualization of clusters
-myCol <- c("darkblue", "purple", "green", "orange")
-scatter(dapc1, scree.da = FALSE, bg="white", pch=20, cell=0, cstar=0, col=myCol, solid=0.4, cex=1, clab=0, leg=TRUE, txt.leg = (paste("Cluster", 1:4)))
-
-##########
-## Tajimas'D with popGenome
-
-# list of populations
-populations <- list("paniscus","troglodytes_ellioti","troglodytes_schweinfurthii","troglodytes_troglodytes","troglodytes_verus")
-# create a GENOME class object with vcf file
-genome <- readData(path = "data/", format = "VCF")
-# function that create a list, which element is the ensemble of individual for each pop
-listing_ind <- function(npan, nellio, nschwein, ntroglo, nverus){
-  pan_vec <- c(rep("NA", npan))
-  ellio_vec <- c(rep("NA", nellio))
-  schwein_vec <- c(rep("NA", nschwein))
-  troglo_vec <- c(rep("NA", ntroglo))
-  verus_vec <- c(rep("NA", nverus))
-  for (i in 1:npan){
-    pan_vec[i] <- paste0("pan_", i)
-  }
-  for (i in 1:nellio){
-    ellio_vec[i] <- paste0("ellioti_", i)
-  }
-  for (i in 1:nschwein){
-    schwein_vec[i] <- paste0("schwein_", i)
-  }
-  for (i in 1:ntroglo){
-    troglo_vec[i] <- paste0("troglo_", i)
-  }
-  for (i in 1:nverus){
-    verus_vec[i] <- paste0("verus_", i)
-  }
-  populations <- list(pan_vec, ellio_vec, schwein_vec, troglo_vec, verus_vec)
-}
-# apply listing_ind with the rignt number of individuals for each pop
-# and save it in the object 'populations'
-populations <- listing_ind(11, 10, 6, 4, 5)
-# set populations in the genome object
-genome <- set.populations(genome, new.populations = populations, diploid = TRUE)
-# check
-genome@populations
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