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GlobalDiversityScript.R
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GlobalDiversityScript.R
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### Multiplot function
#http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
require(Hmisc)
#require(patchwork)
require(ggplot2)
theme_set(theme_bw())
require(reshape2)
require(SNPRelate)
require(gdsfmt)
require(stringr)
require(ggrepel)
require(data.table)
require(ape)
require(directlabels)
require(ggmap)
require(ggtree)
require(phytools)
require(geosphere)
require(gplots)
require(plotrix)
require(ggtree)
require(phangorn)
require(RColorBrewer)
require(vegan)
require(dismo)
require(rasterVis)
require(rgdal)
library(methods)
require(maptools)
require(viridis)
tbb1 = c(7027135,7029293)
tbb2 = c(13433925,13436504)
draft='./data/'
setwd(draft)
draft2='./data/outputdir/' ## Directory where figures will be plotted
climate <- getData('worldclim', var='bio', res=2.5)
## Color list copied from by S. Martin twisst plotting script
cols = c(
"#F0A3FF", #Amethyst
"#0075DC", #Blue
"#993F00", #Caramel
"#4C005C", #Damson
"#191919", #Ebony
"#005C31", #Forest
"#2BCE48", #Green
"#FFCC99", #Honeydew
"#808080", #Iron
"#94FFB5", #Jade
"#8F7C00", #Khaki
"#9DCC00", #Lime
"#C20088", #Mallow
"#003380", #Navy
"#FFA405", #Orpiment
"#FFA8BB", #Pink
"#426600", #Quagmire
"#FF0010", #Red
"#5EF1F2", #Sky
"#00998F", #Turquoise
"#E0FF66", #Uranium
"#740AFF", #Violet
"#990000", #Wine
"#FFFF80", #Xanthin
"#FFFF00", #Yellow
"#FF5005" #Zinnia
)
###--- LatLon for re-ordering
df = read.table(file='./data/Latlon.txt',header=T)
#colnames(df)[1]="sample"
df$reg=substr(df$sample,1,3)
df$iso=substr(df$sample,5,7)
df$iso[df$reg=='FRG']='FRG'
df$iso[df$lon==-0.4648]='FRA.1' ##- 79
df$iso[df$lon==-0.9449]='FRA.2' ##- 64
df$iso[df$lon==2.8415]='FRA.4' ##-63
df$iso[df$lon==2.1486]='FRA.3' ##- 81
df$iso[df$iso=='WAL']='AUS.1'
df$iso[df$iso=='MAR']='MOR'
df$iso[df$iso=='NAP']='AUS.2' ## Isolate was shipped from Napoli, Italy but comes from Australia (also confirmed by genetic data).
df$iso[df$iso=='KOK']='STA.1'
df$iso[df$iso=='OND']='STA.2'
df$iso[df$iso=='WHR']='STA.3'
df$iso2=substr(df$sample,5,7)
write.table(df,file='recoded_isolates.txt',quote=F,row.names=F)
df$geo=''
df$geo[df$iso2 %in% c("FRA", "ACO", "MAR")]='Mediterranean.Area'
df$geo[df$iso2 %in% c("IND", "WAL","NAP")]='Oceania'
df$geo[df$iso2 %in% c("BRA","FRG")]='South.America'
df$geo[df$iso2 %in% c("WHR","KOK", "OND", "NAM","ZAI")]='Sub.Tropical.Africa'
df$geo[df$iso2 %in% c("CAP","STO","BEN")]='Western.Africa'
df$geo[df$reg=='FRA']='Mediterranean.Area'
df$geo[df$reg=='FRG']='South.America'
df$geo=factor(df$geo)
###-- Coverage Nuclear genome
cov = read.table(file='./data/filt_upd_metadata.txt')
cov$sample = sapply(str_split(cov$V1,"/"),function(x) x[length(x)-1])
cov$mcov = cov$V6
df = merge(df,cov[,c('mcov','sample')],by='sample')
###--- Supplementary table 1
#- mito cov
setwd("./data/MITO/")
coverage=read.table(file='cov_stats.txt',header=F,sep='\t')
colnames(coverage)=c('sample','total','mean',
'granular_third_quartile', 'granular_median', 'granular_first_quartile',
'p_bases_above_15')
suptab1 = merge(df,coverage[,c("sample","mean")],by="sample")
#- Lane info
seq = read.table('./data/seq_metadata.out',sep="\t",fill=TRUE)
colnames(seq) = c("sample","Run information")
suptab1 = merge(suptab1,seq,by="sample")
write.csv(suptab1,file=paste0(draft,"supplementary_table1.csv"))
###--- Color definition
clcol = c('#5ab4ac','#ca0020','#d8b365','#252525','#8c510a')
clcol2 = c('#01665e','#ca0020','#d8b365','#252525','#8c510a')
chrom.colors = c('#440154FF','#3B528BFF','#21908CFF','#5DC863FF','#FDE725FF') #c(cols[1],cols[12],cols[2],cols[3],cols[20])
chromlist = c('1_Celeg_TT','2_Celeg_TT','3_Celeg_TT','4_Celeg_TT','5_Celeg_TT_axel')
##-- genome size
gensize = 45778363 + 47384193 + 43564237 + 51819793 + 48825595
nsnp = 23868644
nsnp/gensize
####---- MAP
hcon = unique(df[,c('lon','lat')])
h2 = unique(df[,c('lon','lat','geo','iso')])
h2$Cluster = h2$geo
h2$res = "Fenbendazole-resistant and ivermectin-susceptible"
h2$res[h2$iso=='STA.1'|h2$iso=='STA.3'|h2$iso=='AUS.1']="Fenbendazole- and ivermectin-resistant"
h2$res[h2$iso=='FRA.2'|h2$iso=='FRA.4'|h2$iso=='STO'|h2$iso=='ZAI']="Fenbendazole- and ivermectin-susceptible"
h2$res = factor(h2$res)
levels(h2$res) = levels(h2$res)[c(2,1,3)]
h2$Resistance=17
h2$Resistance[h2$res==levels(h2$res)[2]] = 15
h2$Resistance[h2$res==levels(h2$res)[3]] = 19
h2$Resistance = factor(h2$Resistance,labels=levels(h2$res))
h2$iso[h2$iso=='FRG'] = 'GUA'
pdf(file=paste0(draft2,'Figure1a.pdf'),width=14,height=8)
#Using GGPLOT, plot the Base World Map
mp <- NULL
mapWorld <- borders("world", colour="gray50", fill="white") # create a layer of borders
mp <- ggplot() + mapWorld
#Now Layer the cities on top
mp <- mp + geom_point(data=h2,aes(x=lon, y=lat ,color=Cluster,shape=Resistance), size=3) +
xlab("Longitude") + ylab("Latitude") +
geom_label_repel(data=h2,segment.alpha = 0.8,size=4,
aes(x=lon, y=lat, col=Cluster,label=iso),show.legend = FALSE) +
scale_color_manual(values=clcol) +
theme(legend.position=c(.1,.2),text=element_text(size=14))
mp
invisible(dev.off())
####---- Associate climatic information with each isolate
# coords <- data.frame(x=hcon$lon,y=hcon$lat)
# points <- SpatialPoints(coords, proj4string = climate@crs)
# values <- extract(climate,points)
# bioclim <- cbind.data.frame(coordinates(points),values)
# colnames(bioclim)[1]='lon'
# colnames(bioclim)[2]='lat'
#
# env=merge(df,bioclim,by=c('lat','lon'))
# env$ivm=0
# env$ivm[env$iso2=='WAL']=1
# env$ivm[env$iso2=='KOK'|env$iso2=='WHR']=1
# write.table(env,file='./data/geo_clim_drug_info.txt',quote=F,sep="\t",row.names = FALSE)
env = read.table(file='./data/geo_clim_drug_info.txt',header=T)
#####====== Phylogenetic inference (mtDNA) - Figure 2a ====########
library(ggtree)
require(ape)
require(phytools)
setwd('./data/MITO/')
###-- Rooted Tree
tree0 <- read.tree(file="./pop.muscle.phy_phyml_tree.txt")
plot(density((as.numeric(as.character(tree0$node.label))),na.rm=T))
summary(as.numeric(as.character(tree0$node.label)))
## Rooted tree
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 0.00 14.00 57.00 54.38 98.00 100.00 1
###-- Unrooted Tree // branch-support is improved
tree <- read.tree(file="./pop_unroot.muscle.phy_phyml_tree.txt")
plot(density((as.numeric(as.character(tree$node.label))),na.rm=T))
summary(as.numeric(as.character(tree$node.label)))
## Unrooted tree
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 0.00 19.75 60.50 57.96 100.00 100.00 1
##--- Get iso names
tree$tip.label = df$iso[match(tree$tip.label,df$sample)]
tree$tip.label[tree$tip.label=='FRG']='GUA' ## Modify Guadeloupe encoding
cls = list(c1=c("FRA.1","FRA.2","FRA.3","FRA.4","POR","MOR"),
c2=c("IND", "AUS.1","AUS.2"),
c3=c("BRA","GUA"),
c4=c("STA.1","STA.2","STA.3","NAM","ZAI","ISE"),
c5=c("BEN", "CAP","STO"))
tree = groupOTU(tree,cls)
nodsize = as.numeric(as.character(tree$node.label))/20
nodcol=array('#de2d26',length(tree$node.label))
nodcol[which(as.numeric(as.character(tree$node.label))>=70)] = '#41b6c4'
while(0 %in% levels(attributes(tree)$group)){
attributes(tree)$group[attributes(tree)$group==0] = attributes(tree)$group[which(attributes(tree)$group==0)-1]
attributes(tree)$group = factor(attributes(tree)$group)
}
pdf(file=paste0(draft2,'Figure2a.pdf'),height=10,width=8)
print(ggtree(tree,aes(color = group),layout='daylight') +
#geom_tiplab(size = 2) +
scale_colour_manual(values = clcol,name = "Location",breaks = cls) +
geom_treescale(y = -.1,offset = 0.001) +
geom_nodepoint(color = nodcol, alpha = 0.2, size = nodsize))
dev.off()
rm(tree,tree0)
#####====== Twisst topology plot - Figure 3b ====########
## Twisst plotting function from Martin et al. 2016
setwd("./data/NUC/TW/")
simple.loess.predict <- function(x, y, span, weights = NULL, max = NULL, min = NULL){
y.loess <- loess(y ~ x, span = span, weights = weights)
y.predict <- predict(y.loess,x)
if (is.null(min) == FALSE) {y.predict = ifelse(y.predict > min, y.predict, min)}
if (is.null(max) == FALSE) {y.predict = ifelse(y.predict < max, y.predict, max)}
y.predict
}
smooth_df <- function(x, df, span, col.names=NULL, weights=NULL, min=NULL, max=NULL){
smoothed <- df
if (is.null(col.names)){col.names=colnames(df)}
for (col.name in col.names){
print(paste("smoothing",col.name))
smoothed[,col.name] <- simple.loess.predict(x,df[,col.name],span = span, max = max, min = min, weights = weights)
}
smoothed
}
stack <- function(mat){
upper <- t(apply(mat, 1, cumsum))
lower <- upper - mat
list(upper=upper,lower=lower)
}
interleave <- function(x1,x2){
output <- vector(length= length(x1) + length(x2))
output[seq(1,length(output),2)] <- x1
output[seq(2,length(output),2)] <- x2
output
}
plot_weights <- function(weights_dataframe,positions=NULL,line_cols=NULL,fill_cols=NULL,xlim=NULL,ylim=c(0,1),stacked=FALSE,cex.lab=2,
ylab="Weights", xlab = "Position (Kbp)", main="",xaxt=NULL,yaxt=NULL,bty="n", add=FALSE){
#get x axis
x = positions
#if a two-column matrix is given - plot step-like weights with start and end of each window
if (is.matrix(x)==TRUE) {
x = interleave(positions[,1],positions[,2])
yreps=2
}
else {
if (is.null(x)==FALSE) x = positions
else x = 1:nrow(weights_dataframe)
yreps=1
}
#set x limits
if(is.null(xlim)) xlim = c(min(x), max(x))
#if not adding to an old plot, make a new plot
if (add==FALSE) plot(0, pch = "", xlim = xlim, ylim=ylim, ylab=ylab, xlab=xlab, main=main,xaxt=xaxt,yaxt=yaxt,bty=bty)
if (stacked == TRUE){
y_stacked <- stack(weights_dataframe)
for (n in 1:ncol(weights_dataframe)){
y_upper = rep(y_stacked[["upper"]][,n],each=yreps)
y_lower = rep(y_stacked[["lower"]][,n],each = yreps)
polygon(c(x,rev(x)),c(y_upper, rev(y_lower)), col = fill_cols[n], border=NA)
}
}
else{
for (n in 1:ncol(weights_dataframe)){
y = rep(weights_dataframe[,n],each=yreps)
polygon(c(x,rev(x)),c(y, rep(0,length(y))), col=fill_cols[n], border=NA)
lines(x,y, type = "l", col = line_cols[n])
}
}
}
options(scipen = 5)
## input data for the beta-tubulin region
#CHROM.1.5800000:8000000.bzr.weights.txt
dir='NAM_FRG_FRA_STO'
##-- Read keep list
kl = read.table(file=paste0('./',dir,'/keep.list'),header=F,sep=' ')
kl$cov = kl$V9
#weights file with a column for each topology
w=readLines(paste0('./',dir,'/CHROM.1.5800000:8000000.bzr.weights.csv.gz'))
w2=w[-c(1,2,3)]
weights = read.csv(textConnection(w2),header=TRUE,sep='\t')
#coordinates file for each window
window_data_file <- paste0('./',dir,"/CHROM.1.5800000:8000000.bzr.raxml_bionj.w50.data.tsv")
#normalise rows so weights sum to 1
weights <- weights / apply(weights, 1, sum)
#retrieve the names of the topologies
topoNames = names(weights)
window_data = read.table(window_data_file, header = T)
s = 7016384-50e3
e = 7039311+50e3
weights = weights[window_data$mid<e & window_data$mid>s,]
window_data = window_data[window_data$mid<e & window_data$mid>s,]
#exclude any rows where data is missing
good_rows = which(is.na(apply(weights,1,sum)) == F)
weights <- weights[good_rows,]
window_data = window_data[good_rows,]
## plot raw data
#plot raw data in "stepped" style, with polygons stacked.
#specify stepped style by providing a matrix of starts and ends for positions
pdf(file = paste0(draft2,"Figure3b.pdf"), width = 10, height = 4)
par(mar = c(4,4,1,1))
plot_weights(weights_dataframe=weights, positions=cbind(window_data$start/1000,window_data$end/1000),
line_cols=cols, fill_cols=cols, stacked=T,cex.lab=2,xlim =c(s/1000,e/1000))
abline(v=7027384/1000,col='white',lty=2) #- btub
abline(v=7029311/1000,col='white',lty=2) #- btub
dev.off()
## plot topologies
library(ape)
topos = read.tree(file=paste0("./",dir,"/topologies.CHROM.1.5800000:8000000.bzr.trees"))
for (i in 1:length(topos)) topos[[i]] <- root(topos[[i]], "NAM", resolve.root = T)
pdf(file = paste0("topos.rooted.",dir,".pdf"), width = 5, height = 2)
par(mfrow = c(1,3), mar = c(1,1,2,1), xpd=NA)
for (n in 1:length(topos)){
plot.phylo(topos[[n]], type = "clad", edge.color=cols[n], edge.width=5, label.offset=.1, cex = 1.)
mtext(side=3,text=paste0("topology",n))
}
dev.off()
######---------------- Same plot for another topology
dir='NAM_FRG_FRA_MOR'
kl = read.table(file=paste0('./',dir,'/keep.list'),header=F,sep=' ')
kl$cov = kl$V9
#weights file with a column for each topology
w=readLines(paste0('./',dir,'/CHROM.1.5800000:8000000.bzr.weights.csv.gz'))
w2=w[-c(1,2,3)]
weights = read.csv(textConnection(w2),header=TRUE,sep='\t')
#weights_file <- "CHROM.1.5800000:8000000.bzr.weights.txt"
#coordinates file for each window
window_data_file <- paste0('./',dir,"/CHROM.1.5800000:8000000.bzr.raxml_bionj.w50.data.tsv")
## read data
#weights = read.table(weights_file, header = T)
#normalise rows so weights sum to 1
weights <- weights / apply(weights, 1, sum)
#retrieve the names of the topologies
topoNames = names(weights)
window_data = read.table(window_data_file, header = T)
weights = weights[window_data$mid<e & window_data$mid>s,]
window_data = window_data[window_data$mid<e & window_data$mid>s,]
#exclude any rows where data is missing
good_rows = which(is.na(apply(weights,1,sum)) == F)
weights <- weights[good_rows,]
window_data = window_data[good_rows,]
## plot raw data
#plot raw data in "stepped" style, with polygons stacked.
#specify stepped style by providing a matrix of starts and ends for positions
pdf(file = paste0(draft2,"supplementary_figure11.pdf"), width = 10, height = 4)
par(mar = c(4,4,1,1))
plot_weights(weights_dataframe=weights, positions=cbind(window_data$start/1000,window_data$end/1000),
line_cols=cols, fill_cols=cols, stacked=T,cex.lab=2,xlim =c(s/1000,e/1000))
abline(v=7027384/1000,col='white',lty=2) #- btub
abline(v=7029311/1000,col='white',lty=2) #- btub
dev.off()
## plot topologies
library(ape)
topos = read.tree(file=paste0("./",dir,"/topologies.CHROM.1.5800000:8000000.bzr.trees"))
for (i in 1:length(topos)) topos[[i]] <- root(topos[[i]], "NAM", resolve.root = T)
pdf(file = paste0("topos.rooted.",dir,".pdf"), width = 5, height = 2)
par(mfrow = c(1,3), mar = c(1,1,2,1), xpd=NA)
for (n in 1:length(topos)){
plot.phylo(topos[[n]], type = "clad", edge.color=cols[n], edge.width=5, label.offset=.1, cex = 1.)
mtext(side=3,text=paste0("topology",n))
}
dev.off()
#####====== Pop CLUSTERING using VQSR SNP - 223 samples ====########
setwd("./data/NUC/PCA/")
chr='WG'
### Creation of a gds file
#ped.fn<-paste("GENO.VQSR.MAF5.",chr,".ped.gz",sep="")
#map.fn<-paste("GENO.VQSR.MAF5.",chr,".fmt.map.gz",sep="")
# snpgdsPED2GDS(ped.fn, map.fn,paste("FLT_VQSR.fmt",chr,".gds",sep=""),
# family=TRUE, snpfirstdim=FALSE,
# compress.annotation="ZIP_RA.max", compress.geno="", verbose=TRUE)
### File available on figshare repository: 10.6084/m9.figshare.7923512
genofile<-snpgdsOpen(paste('FLT_VQSR.fmt',chr,'.gds',sep=''))
mafq = 0.05
callrate = 0.5
#LD based SNP pruning
set.seed(1000)
snpset = snpgdsLDpruning(autosome.only = T,genofile,
ld.threshold = .2, maf = mafq, remove.monosnp = T, missing.rate = 1-callrate)
# SNP pruning based on LD:
# Excluding 0 SNP on non-autosomes
# Excluding 7,708,668 SNPs (monomorphic: TRUE, MAF: 0.05, missing rate: 0.5)
# Working space: 223 samples, 411,574 SNPs
# using 1 (CPU) core
# sliding window: 500,000 basepairs, Inf SNPs
# |LD| threshold: 0.2
# method: composite
# Chromosome 1: 0.17%, 2,596/1,567,226
# Chromosome 2: 0.18%, 2,892/1,569,964
# Chromosome 3: 0.18%, 2,586/1,473,245
# Chromosome 4: 0.17%, 2,976/1,739,874
# Chromosome 5: 0.16%, 2,843/1,769,933
# 13,893 markers are selected in total.
snpid = unlist(snpset)
ccm_pca<-snpgdsPCA(snp.id = snpid, genofile,autosome.only=T,
maf = mafq,
missing.rate = 1-callrate,
num.thread=2)
# Principal Component Analysis (PCA) on genotypes:
# Excluding 8,017,187 SNPs (non-autosomes or non-selection)
# Excluding 0 SNP (monomorphic: TRUE, MAF: 0.05, missing rate: 0.5)
# Working space: 223 samples, 102,946 SNPs
isolate=df$iso[match(ccm_pca$sample.id,df$sample)]
#isolate[substr(isolate,1,3)=='FRA']=""
Origin=df$geo[match(ccm_pca$sample.id,df$sample)]
npop=length(unique(isolate))
pc.percent <- 100 * ccm_pca$eigenval/sum(ccm_pca$eigenval,na.rm=T)
pc.percent
p1=ggplot(data.frame(ccm_pca$eigenvect),
aes(x=ccm_pca$eigenvect[,1],
y=ccm_pca$eigenvect[,2],
label=ccm_pca$sample.id,
color=Origin)) +
geom_point(size=2) +
geom_text_repel(segment.alpha = 0.2,size=3,aes(x=ccm_pca$eigenvect[,1],y=ccm_pca$eigenvect[,2],col=Origin,label=isolate)) +
theme(legend.position='none') +
scale_color_manual(values=clcol) +
labs(x = paste("PC1 -",round(pc.percent[1],2),' %',sep='')) +
labs(y = paste("PC2 -",round(pc.percent[2],2),' %',sep='')) +
theme(text=element_text(size=12))
print(p1)
pdf(file=paste0(draft2,"Supplementary_Figure3.pdf"),width=14,height = 8)
print(p1)
dev.off()
rm(p1)
closefn.gds(genofile)
#####====== Pop CLUSTERING using ANGSD - ALL samples ====########
setwd("./data/PCA/")
chr='GW_ld'
## Data generated using ANGSD (ANGSD_223.sh)
## LD estimation for low coverage data was implemented following Bilton et al. 2018 with ld_lowcov_splitter.py & LD_estim_lowcov.R
# Read input file
covar <- read.table(paste0('./ALL',chr,'.covar'), stringsAsFact=F)
# Read annot file
annot <- read.table('./InfoPop.clst', sep="\t", header=T); # note that plink cluster files are usually tab-separated
# Eigenvalues
eig <- eigen(covar, symm=TRUE);
eig$val <- eig$val/sum(eig$val);
cat(signif(eig$val, digits=3)*100,"\n");
plot(density(RMTstat::rtw(223,beta=1)))
# Tracy Widom test
#test each dimension
m=length(eig$values)
a=eig
res <- matrix(data=NA,nrow=(m-1),ncol=5)
colnames(res) <- c("Dimension","Eigenvalue","nhat","TWstat","P-value")
for (j in 1:(m-1)){
L1 <- sum(a$values[j:m])
L2 <- sum(a$values[j:m]^2)
lambda <- a$values[j]*(m-j)/L1
nhat <- L1^2/L2
mu <- (sqrt(nhat-1)+sqrt(m-j))^2/nhat
sigma <- (sqrt(nhat-1)+sqrt(m-j))/nhat*(1/sqrt(nhat-1)+1/sqrt(m-j))^(1/3)
twstat <- (lambda-mu)/sigma
twpvalue <- RMTstat::ptw(twstat,lower.tail=FALSE)
res[j,1] <- j
res[j,2] <- lambda
res[j,3] <- nhat
res[j,4] <- twstat
res[j,5] <- twpvalue
}
res[res[,5]<0.05,]
# Dimension Eigenvalue nhat TWstat P-value
# [1,] 1 7.013588 152.7290 22.93302 0
# [2,] 2 5.591693 168.9922 11.68487 0
rm(m,a)
# Parse components to analyze
comp = c(1,2) #<- as.numeric(strsplit(opt$comp, "-", fixed=TRUE)[[1]])
# Plot
PC <- as.data.frame(eig$vectors)
colnames(PC) <- gsub("V", "PC", colnames(PC))
PC$Pop <- factor(annot$CLUSTER)
x_axis = paste0("PC",comp[1])
xtitle=paste0("PC",comp[1]," (",signif(eig$val[comp[1]], digits=3)*100,"%)")
y_axis = paste0("PC",comp[2])
ytitle=paste0("PC",comp[2]," (",signif(eig$val[comp[2]], digits=3)*100,"%)")
PC$Origin=factor(df$geo[match(PC$Pop,df$iso)])
PC$Pop = gsub('FRG','GUA',PC$Pop)
## Figure 1b
pdf(file=paste0(draft2,'Figure1b.pdf'),width=14,height=8)
ggplot(data=PC, aes_string(x=x_axis, y=y_axis, color=PC$Origin)) +
theme_bw() + ylab(ytitle) + xlab(xtitle) +
scale_color_manual(values=clcol) +
geom_point(size=3) +
geom_text_repel(segment.alpha = 0.2,size=3,
aes(col=Origin,label=Pop)) +
#theme(legend.position='none',axis.title = element_text(size=16))
theme(legend.position=c(.9,0.1),axis.title = element_text(size=16)) +
labs(colour='Geographical origin')
dev.off()
euc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))
a=matrix(0,223,223)
for(i in 1:nrow(PC)){
for(j in 1:nrow(PC)){
a[i,j]=euc.dist(PC$PC1[i],PC$PC2[j])
}
}
colnames(a)=df$sample
rownames(a)=df$sample
###------------------ Phylogeography ?
dmx=matrix(0,223,223)
for(i in 1:dim(df)[1]){
for(j in 1:dim(df)[1]){
lon1=df$lon[i]
lat1=df$lat[i]
lon2=df$lon[j]
lat2=df$lat[j]
dmx[i,j] = distm(c(lon1, lat1), c(lon2, lat2), fun = distHaversine)/1000
}
}
rownames(dmx)=df$sample
colnames(dmx)=df$sample
mtest1=mantel(dmx,a,method = 'spearman')
mtest1
# Call:
# mantel(xdis = dmx, ydis = a, method = "spearman")
#
# Mantel statistic r: 0.06865
# Significance: 0.003
#
# Upper quantiles of permutations (null model):
# 90% 95% 97.5% 99%
# 0.0285 0.0355 0.0411 0.0523
# Permutation: free
# Number of permutations: 999
#####====== Pop CLUSTERING using mitochondrial information ====########
setwd('./MITO/')
#ped.fn<-"MITO.MAF5.ped"
#map.fn<-"MITO.MAF5.map"
#snpgdsPED2GDS(ped.fn, map.fn,"MITO.MAF5.gds",
# family=TRUE, snpfirstdim=FALSE,
# compress.annotation="ZIP_RA.max", compress.geno="", verbose=TRUE)
genofile<-snpgdsOpen('MITO.MAF5.gds')
mafq=0.05
callrate=0.1
ccm_pca<-snpgdsPCA(genofile,autosome.only=FALSE,
maf = mafq, missing.rate = callrate,
num.thread=2)
isolate=df$iso[match(ccm_pca$sample.id,df$sample)]
isolate[substr(isolate,1,3)=='FRA']=""
isolate[isolate=='FRG']="GUA"
Origin=df$geo[match(ccm_pca$sample.id,df$sample)]
npop=length(unique(isolate))
pc.percent <- 100 * ccm_pca$eigenval/sum(ccm_pca$eigenval,na.rm=T)
pc.percent
pdf(file=paste0(draft2,'supplementary_Figure3.pdf'),width=14, height=8)
p1=ggplot(data.frame(ccm_pca$eigenvect),
aes(x=ccm_pca$eigenvect[,1],
y=ccm_pca$eigenvect[,2],
label=ccm_pca$sample.id,
color=Origin)) +
geom_point(size=3) +
geom_text_repel(segment.alpha = 0.2,size=3,aes(x=ccm_pca$eigenvect[,1],y=ccm_pca$eigenvect[,2],col=Origin,label=isolate)) +
theme(legend.position='none') +
scale_color_manual(values=clcol) +
labs(x = paste("PC1 -",round(pc.percent[1],2),' %',sep='')) +
labs(y = paste("PC2 -",round(pc.percent[2],2),' %',sep='')) +
theme(text=element_text(size=16))
p2=ggplot(data.frame(ccm_pca$eigenvect),
aes(x=ccm_pca$eigenvect[,3],
y=ccm_pca$eigenvect[,2],
label=ccm_pca$sample.id,
color=Origin)) +
geom_point(size=3) +
geom_text_repel(segment.alpha = 0.2,size=3,
aes(x=ccm_pca$eigenvect[,3],y=ccm_pca$eigenvect[,2],
col=Origin,label=isolate)) +
theme(legend.position=c(.8,.9)) + scale_color_manual(values=clcol) +
labs(x = paste("PC3 -",round(pc.percent[3],2),' %',sep='')) +
labs(y = paste("PC2 -",round(pc.percent[2],2),' %',sep='')) +
theme(text=element_text(size=16))
multiplot(p1,p2,cols=2)
dev.off()
closefn.gds(genofile)
#####====== ADMIXTURE USING ANGSD - ALL samples ====########
##-- Analysis run with ANGSD (ANGSD_223.sh)
##-- LD estimation for low coverage data was implemented following Bilton et al. 2018 with ld_lowcov_splitter.py & LD_estim_lowcov.R
setwd("./data/NUC/ADMXITURE/")
rep = "./data/NUC/ADMXITURE/"
prefix = "ALL"
k = seq(2,10,1)
k = 3
###--- Admxiture plot for Figure 2c
K = 3
chr='GW_ld'
infile=paste(rep,prefix,".",chr,".",K,".qopt",sep="")
outfile = paste0(draft2,"Figure2c.pdf")
# Admixture
admix <- t(as.matrix(read.table(infile)))
K <- nrow(admix)
pdf(file=outfile,width=14,height=3)
samp<-read.table(paste(rep,"sample.info",sep=""),as.is=T)
pop = df[match(samp$V1,df$sample),c('sample','lon','iso')]
admix<-admix[,order(pop[,2])]
pop<-pop[order(pop[,2]),]
pop$rn=seq(1:dim(pop)[1])
ord=unique(pop[,3])
bd = pop$rn[which(row.names(pop) %in% row.names(unique(pop[,2:3])))]
lab = array('',dim(pop)[1])
lab[bd]=pop$iso[bd]
colnames(admix)=pop$sample
m=melt(admix)
colnames(m)=c("K","Population","Admixture")
lab = gsub('FRG','GUA',lab)
print(ggplot(m,aes(x=Population,y=Admixture,fill=K,col=K))+
geom_bar(stat='identity') +
scale_fill_manual(values=clcol[c(3,1,4)]) +
scale_color_manual(values=clcol[c(3,1,4)]) +
geom_vline(xintercept=bd-0.5,colour="white") +
scale_y_continuous(expand=c(0,0)) +
theme_classic() + #coord_flip() +
theme(legend.position='none',
axis.text.x = element_text(angle = 90, hjust = 1),
text=element_text(size=18)) + xlab('')+
scale_x_discrete(breaks=pop$sample,labels=lab))
invisible(dev.off())
##--- Supplementary Figure 5: admixture plots for various K
## This yields 1 pdf file for each K; to be combined into a single plot for Fig. S5.
k=c(2,seq(4,10,1))
chr='GW_ld'
for(K in k){
infile=paste(rep,prefix,".",chr,".",K,".qopt",sep="")
outfile=paste(rep,prefix,".",chr,".",K,".ALL.pdf",sep="")
# Admixture
admix <- t(as.matrix(read.table(infile)))
K <- nrow(admix)
samp<-read.table(paste(rep,"sample.info",sep=""),as.is=T)
pop = df[match(samp$V1,df$sample),c('sample','lon','iso')]
admix<-admix[,order(pop[,2])]
pop<-pop[order(pop[,2]),]
pop$rn=seq(1:dim(pop)[1])
ord=unique(pop[,3])
bd = pop$rn[which(row.names(pop) %in% row.names(unique(pop[,2:3])))]
lab = array('',dim(pop)[1])
lab[bd]=pop$iso[bd]
colnames(admix)=pop$sample
m = melt(admix)
colnames(m) = c("K","Population","Admixture")
lab = gsub('FRG','GUA',lab)
pdf(file=outfile,width=14,height=2)
print(ggplot(m,aes(x=Population,y=Admixture,fill=K,col=K))+
geom_bar(stat='identity') + #ggtitle(paste0('K=',K))+
scale_fill_manual(values=cols) +
scale_color_manual(values=cols) +
geom_vline(xintercept=bd-0.5,colour="white") +
scale_y_continuous(expand=c(0,0)) +
theme_classic() + #coord_flip() +
theme(legend.position='none',
axis.text.x = element_text(angle = 90, hjust = 1),
text=element_text(size=16)) + xlab('')+
scale_x_discrete(breaks=pop$sample,labels=lab))
invisible(dev.off())
}
#####====== ADMIXTURE USING ANGSD - Cross-Validation: K = 3 ====########
setwd("./data/NUC/ADMIXTURE/")
rep="./data/NUC/ADMIXTURE/"
prefix="ALL"
k=seq(2,10,1)
chrl=c('GW_ld.CV1','GW_ld.CV2','GW_ld.CV3','GW_ld.CV4','GW_ld.CV5')
cv1 = array(-1,length(k))
cv2 = array(-1,length(k))
for(K in k){
for(chr in chrl){
infile=paste(rep,prefix,".",chr,".",K,".qopt",sep="")
# Admixture
assign(paste0('admix.',chr),t(as.matrix(read.table(infile))))
}
# Med absolute deviation
cv1[K-1] = mad(c(admix.GW.CV1,admix.GW.CV2,admix.GW.CV3,admix.GW.CV4,admix.GW.CV5))
# Jackniffing variance
cv2[K-1] = var(c(admix.GW.CV1,admix.GW.CV2,admix.GW.CV3,admix.GW.CV4,admix.GW.CV5))
rm(admix.GW.CV1,admix.GW.CV2,admix.GW.CV3,admix.GW.CV4,admix.GW.CV5)
}
pdf(file=paste0(draft2,'Supplementary_Figure5.pdf'))
plot(k,log10(cv1),pch=19,type='b',ylab='Median absolute deviation')
dev.off()
#####====== Diversity and divergence between populations ====########
rep = './data/MITO/'
####----- Downsampling to 5 individuals // this excludes ACO and BRA
f=table(df$iso[match(covqc$sample_id,df$sample)])
pop=names(f)[which(f[]>=5)]
tot5=c(0,0,0,0)
for(i in 1:length(pop)){
pi=read.table(paste(rep,'PI5.',pop[i],'.windowed.pi',sep=""),header=T)
pi$midpos=pi$BIN_END+(pi$BIN_END-pi$BIN_START)/2
pi=pi[,c('midpos','PI','N_VARIANTS')]
pi$po=pop[i]
tot5=rbind(tot5,pi)
}
tot5=tot5[tot5$po!=0,]
tot5$geo=df$geo[match(tot5$po,df$iso)]
ggplot(tot5,aes(x=midpos,y=PI,col=geo)) +
facet_wrap(~ po) + scale_color_manual(values=clcol)+
geom_point()
aggregate(PI ~ po,FUN=mean,data=tot5)
a=aggregate(PI ~ geo,FUN=mean,data=tot5)
a$std=aggregate(PI ~ geo,FUN=sd,data=tot5)[,2]
a
# geo PI std
# 1 Mediterranean.Area 0.006270674 0.003692800
# 2 Oceania 0.005781422 0.003107405
# 3 South.America 0.006647628 0.003243145
# 4 Sub.Tropical.Africa 0.006021775 0.003461205
# 5 Western.Africa 0.007149930 0.004468009
###--- Test
summary(lm(PI ~ geo,data=tot5))
# Residuals:
# Min 1Q Median 3Q Max
# -0.005240 -0.002388 -0.001247 0.001227 0.023193
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 5.884e-03 1.950e-04 30.171 < 2e-16 ***
# geoOceania 2.293e-04 3.145e-04 0.729 0.466036
# geoSouth.America 6.971e-04 4.323e-04 1.613 0.107065
# geoSub.Tropical.Africa 5.967e-05 2.880e-04 0.207 0.835873
# geoWestern.Africa 1.146e-03 3.046e-04 3.761 0.000178 ***
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 0.0037 on 1227 degrees of freedom
# Multiple R-squared: 0.01422, Adjusted R-squared: 0.01101
# F-statistic: 4.425 on 4 and 1227 DF, p-value: 0.001487
0.0008793/0.0062707*100
#[1] 14.02236
###--------- Nucleotide diversity using ANGSD
rep="./data/NUC/TAJHI"
###---- Summary statistics
theme_set(theme_bw())
t.HI=matrix(0,1,7)
t.HI=data.frame(t.HI)
colnames(t.HI)=c('Chr','WinCenter','tP','Tajima','nSites','cov','pop')
t.HI$Chr=factor(t.HI$Chr,levels=c(chromlist))
for(r in c('FRA.1','FRG','NAM')){
for(c in chromlist){
temp = read.table(paste0(rep,'/',c,'/',r,'.thetas.idx.pestPG'))
temp = temp[,c(2,3,5,9,14)]
colnames(temp) = c('Chr','WinCenter','tP','Tajima','nSites')
temp$cov = 'HI'
temp$pop = r
t.HI = rbind(t.HI,temp)
rm(temp)
}
}
t.HI=na.omit(t.HI)
t.HI=t.HI[t.HI$nSites>500,] #- 10% of win width
piMean=aggregate(tP/nSites ~ pop,FUN=mean,data=t.HI)
piMean$std=aggregate(tP/nSites ~ pop,FUN=sd,data=t.HI)[,2]
piMean
# pop tP/nSites std
# 1 FRA.1 0.006519367 0.0005379378
# 2 FRG 0.010595454 0.0007856049
# 3 NAM 0.011322935 0.0007861508
##-- Ne from pi
mu2=2.7e-9
Ne2=piMean$`tP/nSites`/(4*mu2)
Ne2/1e6
#[1] 0.6036451 0.9810606 1.0484199
##-- Overall distribution
wb = 2.47e-4
ovols = 4e-3
ovoln = 1e-3
human = 0.119/100 #Perry et al. Genome Res2012 PMC3317143/
droso = mean(c(0.00531,0.00752,0.01714)) #Langley 2012 Genetics
mpi = max(t.HI$tP/t.HI$nSites)
##-- Supplementary Figure 1
pdf(file=paste0(draft2,'Supplementary_Figure1.pdf'))
t.HI$pop=factor(t.HI$pop)
ggplot(t.HI,aes(x=pop,y=tP/nSites,col=pop)) +
theme_classic()+theme(text=element_text(size=16)) +
xlab('Population') +
scale_y_continuous(limits=c(0,mpi),breaks=seq(0,mpi,by=0.001)) +
#geom_hline(yintercept = human,col='blue') + #annotate("Human", x=c(human+0.01),y=200) +
geom_hline(yintercept = droso,col='red') + #annotate("D.melanogaster", x=c(droso+0.01),y=200) +
geom_hline(yintercept = wb,col='forestgreen') + #annotate("W.bancrofti", x=c(wb+0.01),y=200) +
geom_hline(yintercept = ovols,col='purple') + #annotate("W.bancrofti", x=c(wb+0.01),y=200) +
geom_hline(yintercept = ovoln,col='pink') + #annotate("W.bancrofti", x=c(wb+0.01),y=200) +
ylab('Nucleotide diversity') +
geom_point(size=3) + theme(legend.position='none') +
scale_color_manual(values=c(clcol[c(1,3,4)]))
dev.off()
rm(t.HI)
###----- Diversity values for ALL populations
rep = "./data/NUC/TAJ/"
iso5 = unique(df$iso[which(df$iso!='BRA' & df$iso!='FRA.4')]) ## discard pop with less than 5 individuals
###---- Summary statistics
theme_set(theme_bw())
t.ALL=matrix(0,1,7)
t.ALL=data.frame(t.ALL)
colnames(t.ALL)=c('Chr','WinCenter','tP','Tajima','nSites','cov','pop')
t.ALL$Chr=factor(t.ALL$Chr,levels=c(chromlist))
for(r in iso5){
for(c in chromlist){
temp = read.table(paste0(rep,'/',c,'/',r,'.thetas.idx.pestPG'))
temp = temp[,c(2,3,5,9,14)]
colnames(temp) = c('Chr','WinCenter','tP','Tajima','nSites')
temp$cov = 'HI'
temp$pop = r
t.ALL = rbind(t.ALL,temp)
rm(temp)
}
}
t.ALL=na.omit(t.ALL)
piMean=aggregate(tP/nSites ~ pop,FUN=mean,data=t.ALL)
piMean$std=aggregate(tP/nSites ~ pop,FUN=sd,data=t.ALL)[,2]
a = aggregate(mcov ~ iso,FUN=mean,data=df)
piMean$mcov = a$mcov[match(piMean$pop,a$iso)]
piMean
# pop tP/nSites std mcov
# 1 ACO 0.006807788 0.0009633793 1.7920000
# 2 AUS.1 0.006132598 0.0004379338 2.5666667
# 3 AUS.2 0.005093270 0.0003204319 2.7360000
# 4 BEN 0.009389602 0.0023180484 0.3300000
# 5 CAP 0.007210416 0.0010416897 0.7042857
# 6 FRA.1 0.007231135 0.0003690067 3.3248571
# 7 FRA.2 0.005439821 0.0005930883 1.8166667
# 8 FRA.3 0.006230038 0.0008429861 0.4833333
# 9 FRG 0.010777981 0.0003249374 4.3986957
# 10 IND 0.005551998 0.0007544601 4.9500000
# 11 MOR 0.008197893 0.0004382342 3.5000000
# 12 NAM 0.013001924 0.0007489163 5.6153333
# 13 STA.1 0.006692275 0.0018268375 4.0071429
# 14 STA.2 0.004417365 0.0004476453 1.7914286
# 15 STA.3 0.011243171 0.0008985027 2.6376923
# 16 STO 0.009746861 0.0007465504 2.6133333
# 17 ZAI 0.008575769 0.0002907212 2.1080000
summary(piMean$`tP/nSites`)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.004417 0.006133 0.007210 0.007749 0.009390 0.013002
###-- biased by coverage // strange case of Indonesia
ggplot(piMean,aes(x=`tP/nSites`,y=mcov)) +
geom_smooth(method='lm') +
geom_point(aes(col=pop))
summary.aov(lm(tP/nSites ~ mcov + Chr,data = t.ALL))
# Df Sum Sq Mean Sq F value Pr(>F)
# mcov 1 0.0000542 5.418e-05 9.497 0.00283 **
# Chr 4 0.0000180 4.510e-06 0.790 0.53497
# Residuals 79 0.0004507 5.700e-06
rcorr(piMean$`tP/nSites`,piMean$mcov)
# x y
# x 1.00 0.34
# y 0.34 1.00
#
# n= 17
# P
# x y
# x 0.1783
# y 0.1783