/
AnalysisMaMi_2022_clean.R
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AnalysisMaMi_2022_clean.R
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#******************************************
#******************************************
### Analysis Paper "A global analysis of matches and mismatches between human genetic and linguistic histories"
# GeLaTo database
# Chiara Barbieri April 2022
#******************************************
#*
#*
#*
### read the two main files
# list of 397 populations :
perpopRED<-read.table("PerpopRED_MaMi2022.txt", header = T, sep = "\t", as.is=T)
# list of pairwise comparisons :
FstListinfo<-read.table("FstListREDinfo_MaMi2022.txt", header=T, sep="\t")
# color palette for major language families
#
Lfamil<-table(perpopRED$glottolog.NAME)
MainFamilies<-unlist(labels(Lfamil[which(Lfamil>4)])) # minimum 5 populations per Lang Family
sizes<-as.numeric(unlist((Lfamil[which(Lfamil>4)])))
perpopREDfamily<-perpopRED[which(perpopRED$glottolog.NAME %in% MainFamilies),]
colorchoice<-c( "darkorange4" ,"#93032E" , "#33A02C" , "#A6D854" , "#377EB8" , "#E31A1C" , "#FFD92F" , "#FF7F00" , "#666666" ,
"cyan4" , "#BC80BD" , "#FED9A6" , "tan3" , "#6A3D9A" , "deeppink" )
perpopRED$MainFamilies<-NA
perpopRED$MainFamilies[which(perpopRED$glottolog.NAME%in%MainFamilies)]<-perpopRED$glottolog.NAME[which(perpopRED$glottolog.NAME%in%MainFamilies)]
# #*********
# library(RColorBrewer)
# qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
# col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
# colorchoice<-sample(col_vector, length(MainFamilies))
# #*********
MainFamilies2<-as.data.frame(MainFamilies)
MainFamilies2$COLOR<-colorchoice
MainFamilies2$names<-paste0(MainFamilies, " (",sizes, ")" )
library(ggplot2)
# color palette blue and red
darkredYES<-"#BA1E1E"
blueNO<-"#198B9F"
#******************************************
### MAP
#******************************************
library(maps)
library('maps')
library('geosphere')
library(rworldmap)
# Script from Balthasar Bickel to plot maps Pacific Centered
source("ggworld.R") # map for Pacific Center projection, modified from Balthasar Bickel
#
perpopRED$lat<-as.numeric(perpopRED$lat)
perpopRED$lon<-as.numeric(perpopRED$lon)
perpopRED2<-perpopRED[!is.na(perpopRED$lat),] # exclude populations for which i do not have geographic coordinates
perpopREDSHIFTMAP<-MoveDataCoord(perpopRED2) # perform coordinate shift to plot Pacific centered maps
## map population location and language families
#### MAP WITH MAJOR LANGUAGE FAMILIES ASSIGNED TO COLOR CODE,
# Basic map
#******************************************
#*
# make a base_plot with contained latitudes
base_plot <- ggplot() +
geom_polygon(data = subset(world.sh.tr.df, lat > -60 & lat < 72),
aes(x = X, y = Y, group = group),
fill = base_fill,
color = base_color,
size = .1
) + theme_void()
base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color="black",shape=3,size=0.5)+
theme(legend.position="bottom", legend.text=element_text(size=8), legend.title = element_blank())+
geom_point(data = perpopREDSHIFTMAP[which(perpopREDSHIFTMAP$glottolog.NAME%in%MainFamiliesSHORT),],
aes(x = lon.1, y = lat.1, color=glottolog.NAME),
size = 2, alpha=0.5)+
scale_color_manual(values=colorchoice3, labels = MainFamilies3$names)
ggsave("WholeGelatoMap_Pacific_LittleCrossesMajorFamilies_largerFig2_2022.pdf", useDingbats=FALSE, width = 8.5, height = 5, dpi = 300, units = "in")
#******************************************
#*#******************************************
#* genetic and linguistic enclaves
#*#******************************************
# ***************************************************
# evaluate the incidence of neighbors from the SAME AND THE different L famiy for each population
# closer neighbor from same and different L family
perpopRED$hasOtherMatches<-"YES"
perpopRED$closeFstSameFamily<-NA
perpopRED$geodistSameFamily<-NA
perpopRED$closeFstDIFFFamily<-NA
perpopRED$geodistDIFFFamily<-NA
perpopRED$DIFFFamilyClosestpop<-NA
perpopRED$SameFamilyMostDistantClosestFst<-NA
perpopRED$SamefamilyClosestpop<-NA
# how far the same family is gen closer than other families?
for (i in 1:nrow(perpopRED)){
targetpop<-perpopRED$PopName[i]
temp<-FstListinfo[which(FstListinfo$Pop1==targetpop),]
samefamily<-temp[which(temp$FAMILY!="DIVERSE"),]
escludiniVicini<-which(samefamily$GEOdist<10&samefamily$glottocodeBase1==samefamily$glottocodeBase2)
if(length(escludiniVicini)!=0){
samefamily<-samefamily[-escludiniVicini,] # exclude when there is a neighbor too close (LESS THAN 10 KM) from same exact language as DUPLICATED SAMPLE
}
if(nrow(samefamily)==0){
perpopRED$hasOtherMatches[i]<-"NO"
}
else{
perpopRED$closeFstSameFamily[i]<-sort(samefamily$FST)[1] # the closest Fst
perpopRED$geodistSameFamily[i]<-samefamily$GEOdist[order(samefamily$FST)][1] # the geographic distance from the closest Fst
perpopRED$SamefamilyClosestpop[i]<-samefamily[order(samefamily$FST),][1,]$Pop2 # the pop which closest fst in match
}
DIFFfamily<-temp[which(temp$FAMILY=="DIVERSE"),]
perpopRED$closeFstDIFFFamily[i]<-sort(DIFFfamily$FST)[1] # the closest Fst from a different language family
perpopRED$geodistDIFFFamily[i]<-DIFFfamily$GEOdist[order(DIFFfamily$FST)][1] # the geographic distance from the closest Fst of a different language family
perpopRED$DIFFFamilyClosestpop[i]<-DIFFfamily[order(DIFFfamily$FST),][1,]$Pop2 # the pop which closest fst in mismatch
if(length(which(sort(samefamily$FST)<perpopRED$closeFstDIFFFamily[i]))==0){
perpopRED$SameFamilyMostDistantClosestFst[i]<-"NONE" }
else{
perpopRED$SameFamilyMostDistantClosestFst[i]<-tail(samefamily$GEOdist[which(sort(samefamily$FST)<perpopRED$closeFstDIFFFamily[i])],1) # the geographic distance from the closest Fst of the same language family that is less than the closest fst from diff L family
}
}
perpopRED$proportionFST_diff_sameFamily<- perpopRED$closeFstDIFFFamily/perpopRED$closeFstSameFamily
perpopRED$proportionGeoDistSameDiffFamily<- perpopRED$geodistDIFFFamily/perpopRED$geodistSameFamily
perpopRED$EnclavesMismatch<-NA
perpopRED$EnclavesMismatch[which(perpopRED$closeFstDIFFFamily==0&perpopRED$closeFstSameFamil!=0)] <- "secondaryMISMATCH_FSTzeroDiffFamily"
perpopRED$EnclavesMismatch[which(perpopRED$proportionFST_diff_sameFamily<1&perpopRED$proportionGeoDistSameDiffFamily>1)] <- "MISMATCH"
perpopRED$EnclavesMismatch[which(perpopRED$proportionFST_diff_sameFamily>1&perpopRED$proportionGeoDistSameDiffFamily<1)] <- "MATCH"
perpopRED$EnclavesMismatch[which(perpopRED$hasOtherMatches!="YES" )]<-"ZeroSameFamilyNeighbors"
table(perpopRED$EnclavesMismatch)/nrow(perpopRED)
### table to visualize how many cases
#************************
## april 2022
MATCH MISMATCH secondaryMISMATCH_FSTzeroDiffFamily ZeroSameFamilyNeighbors
0.130982368 0.070528967 0.005037783 0.055415617
MATCH MISMATCH secondaryMISMATCH_FSTzeroDiffFamily ZeroSameFamilyNeighbors
52 28 2 22
#************************
#Matches
ListMatches<-perpopRED$PopName [which(perpopRED$EnclavesMismatch=="MATCH")]
### List of very drifted populations to flag
DRIFTONI<-perpopRED[which(perpopRED$medianFSTregion>0.1&perpopRED$medianFST>0.1),]$PopName
> DRIFTONI
[1] "Baining_Marabu" "Chukchi" "Eskimo_Sireniki" "Itelmen" "Ju_hoan_North"
[6] "Karitiana" "Koryak" "Lahu" "Nanai" "Nganasan"
[11] "Nganasan_UstAvam" "Nganasan_Volochanka" "Nivh" "Onge" "Rennell_and_Bellona"
[16] "San" "She" "Surui"
## genetic and linguistic enclaves
ListEnclaves<-perpopRED$PopName [which(perpopRED$EnclavesMismatch%in%c("MISMATCH","secondaryMISMATCH_FSTzeroDiffFamily"))]
> ListEnclaves
[1] "Yoruba" "Mengen" "Bengali" "Hazara"
[5] "Kharia" "Gui" "Khwe" "Nama"
[9] "Mongola" "Avar_Gunibsky" "Aleut" "Dai"
[13] "Jew_Georgian" "Khomani" "Spanish_PaisVasco" "Yaquis"
[17] "Yukagir_Forest" "Yukagir_Tundra" "Zapotec" "Wayku"
[21] "Han-NChina" "Evenk_FarEast" "Cocama" "Guarani"
[25] "Guarani_GN" "Karitiana" "Surui" "Azeri_Azerbajan"
[29] "Hungarian1" "Hungarian2"
perpopRED$ListEnclaves<-NA
perpopRED$ListEnclaves[which(perpopRED$PopName%in%ListEnclaves)]<-"Enclave"
#*#******************************************
#*
#* different distribution FST closest same family and different family
#*
aa<-perpopRED[,c(1, which(colnames(perpopRED)%in%c("glottolog.NAME","closeFstSameFamily","closeFstDIFFFamily" )))]
aa$difference<-aa$closeFstSameFamily-aa$closeFstDIFFFamily
length(which(aa$difference>0))/(nrow(perpopRED)-length(which(is.na(aa$difference)))) # only for the comparisons for which i have a same family FST
[1] 0.1973333 ## april 2022
# ~20 % of the pops who have another genetic population of the same language family do have closer FST with speakers of another language family
# *****************************************
# ### figure 1B, target enclaves
# *****************************************
# # target match enclave Kalmyk BedouinB
# target genetic enclave Jew_Georgian
# target linguistic enclave Hungarian
library(reshape)
targetenclaves<-c("Jew_Georgian", "Hungarian1","Himba")
targetenclavesinfo<-perpopRED[which(perpopRED$PopName%in%targetenclaves),]
targetenclavesinfo<-targetenclavesinfo[,which(colnames(targetenclavesinfo)%in%
c( "PopName", "glottolog.NAME","closeFstSameFamily","geodistSameFamily" ,
"SamefamilyClosestpop" ,"closeFstDIFFFamily" , "geodistDIFFFamily" ,"DIFFFamilyClosestpop", "DIFFFamilyClosestFAM"))]
targetenclavesinfo
targetenclavesinfo[2,1]<-"Hungarian"
targetenclavesinfo[3,1]<-"Jewish Georgian"
colnames(targetenclavesinfo)[3]<-"YES"
colnames(targetenclavesinfo)[5]<-"NO"
testt<-melt(targetenclavesinfo[,-c(4,6)])
coso<-melt(targetenclavesinfo[,c(1,4,6)])
testt$GeoDist<-paste0(coso$value, " km")
testt$GeoDistTrue<-coso$value
testt$closestPOP<-testt$SamefamilyClosestpop
testt$closestPOP[which(testt$variable=="NO")]<-testt$DIFFFamilyClosestpop[which(testt$variable=="NO")]
testt$value[which(testt$value==0)]<-0.0001 # or it does not show up in the plot
testt<-testt[1:6,]
# Balthasar version
ggplot(testt, aes(x=GeoDistTrue,y= value, color=variable))+
geom_point(size=4) +
scale_color_manual("Same Family", values=c(darkredYES,blueNO), position="bottom")+
xlab("Geographic distance (km)") +
ylab("Fst") +
theme_minimal()+
facet_wrap(~PopName,scales="free")
ggsave("Fig1b_toyExamplesEnclaves_2022.pdf",height=2.5,width=8, useDingbats=FALSE)
#******************************************
#*#******************************************
#*#******************************************
#*#******************************************
#*#******************************************
#*#******************************************
#* COMPARING MEDIAN FST BETWEEN AND WITHIN
# POPULATION HEURISTIC case 2
#* FST distribution between and within language families MISALIGNED
#*#******************************************
#*#******************************************
## exclude neighbors who speak the same language as DUPLICATE POPS
escludiniVicini<-FstListinfo[which(FstListinfo$GEOdist<10&FstListinfo$glottocodeBase1==FstListinfo$glottocodeBase2),]
> dim(escludiniVicini)
[1] 32 25
escludiniVicini[which(escludiniVicini$case=="single"),][,c(1:3,11:12)]
Pop2 Pop1 FST family2 region2
73128 Kove_Tamuniai Kove 0.000000000 Austronesian OCEANIA
85508 Mamanwa1 Mamanwa 0.050776000 Austronesian SOUTHEAST_ASIA
115332 San Ju_hoan_North 0.001266060 Kxa AFRICA
130035 Sulka_Watwat Sulka_Ganai 0.000000000 Sulka OCEANIA
14937 BedouinB BedouinA 0.017572100 Afro-Asiatic EURASIA
42676 Greek_WGA Greek_Athens 0.003147220 Indo-European EURASIA
45019 GujaratiB GujaratiA 0.001633070 Indo-European EURASIA
45284 GujaratiC GujaratiB 0.003168390 Indo-European EURASIA
45342 GujaratiC GujaratiA 0.002983290 Indo-European EURASIA
46884 Han_HGDP Han-NChina 0.002480980 Sino-Tibetan EURASIA
49935 Hungarian2 Hungarian1 0.000000000 Uralic EURASIA
78287 Lebanese_Muslim Lebanese_Christian 0.001025050 Afro-Asiatic EURASIA
107168 Palestinian Druze 0.008987530 Afro-Asiatic EURASIA
107902 Peru_Quechua Cusco2 0.000427919 Quechuan AMERICAS
150014 Uzbek_Tashkent Uzbek 0.000917217 Turkic EURASIA
150910 Vietnamese_North Kinh 0.001434990 Austroasiatic SOUTHEAST_ASIA
## exclude DRIFTONI
## exclude drifted pops or the Fst averages will be higher than normal
DRIFTONI<-perpopRED[which(perpopRED$medianFSTregion>0.1&perpopRED$medianFST>0.1),]$PopName
FstListREDinfo_noDuplicateNeighbors<-FstListinfo[-which(FstListinfo$GEOdist<10&FstListinfo$glottocodeBase1==FstListinfo$glottocodeBase2),]
FstListREDinfo_noDuplicateNeighborsNoDrift<-FstListREDinfo_noDuplicateNeighbors[-c(which(FstListREDinfo_noDuplicateNeighbors$Pop2%in%DRIFTONI), which(FstListREDinfo_noDuplicateNeighbors$Pop1%in%DRIFTONI)),]
library(pairwiseCI)
perpopRED$MedDiff<-NA
perpopRED$MedDiffCIlower<-NA
perpopRED$MedDiffCIupper<-NA
perpopRED$MedianWithinSMALLER2<-NA
perpopRED$MedDiffCI<-NA
for (i in 1:nrow(perpopRED)){
TARGET<-perpopRED$PopName[i]
meltini<-FstListREDinfo_noDuplicateNeighborsNoDrift[which(FstListREDinfo_noDuplicateNeighborsNoDrift$Pop1==TARGET),]
meltiniSAME<-meltini[which(meltini$SameFamily=="YES"),]
maxgeofam<-max(meltiniSAME$GEOdist, na.rm = T)
if(maxgeofam<500){
maxgeofam<-500
}
meltini<-meltini[which(meltini$GEOdist<=maxgeofam),]
meltiniNO<-meltini[which(meltini$SameFamily!="YES"),]
if(length(which(meltini$SameFamily=="YES"))>2&
length(which(meltini$SameFamily=="NO"))>2){ # need to have at least 3 comparisons between and within family
perpopRED$MedDiff[i]=Median.diff(meltiniNO$FstLinear, meltiniSAME$FstLinear, conf.level=0.95, alternative="lesser",R=10000)$estimate
perpopRED$MedDiffCIlower[i]=Median.diff(meltiniNO$FstLinear, meltiniSAME$FstLinear, conf.level=0.95, alternative="lesser",R=10000)$conf.int[1]
perpopRED$MedDiffCIupper[i]=Median.diff(meltiniNO$FstLinear, meltiniSAME$FstLinear, conf.level=0.95, alternative="lesser",R=10000)$conf.int[2]
perpopRED$MedianWithinSMALLER2[i]<-median(meltiniSAME$FstLinear)<median(meltiniNO$FstLinear) # if TRUE, the median FST within is smaller than the FST between
}
}
perpopRED$MedDiffCI<-perpopRED$MedDiffCIupper-perpopRED$MedDiffCIlower
ListMisaligned<-perpopRED$PopName[which(perpopRED$MedDiff<0)]
length(ListMisaligned)/length(which(!is.na(perpopRED$MedDiff)))
#*#******************************************
[1] 0.2044728 ## 20% of populations in misalignment
#*#******************************************
> length(ListMisaligned)
[1] 64
perpopRED$listSingleCases<-"ND"
perpopRED$listSingleCases[which(perpopRED$PopName%in%ListEnclaves)]<-"Enclave"
perpopRED$listSingleCases[which(perpopRED$PopName%in%ListMisaligned)]<-"Misaligned"
perpopRED$listSingleCases[which(perpopRED$PopName%in%intersect(ListMisaligned, ListEnclaves))]<-"EnclaveANDMisaligned"
perpopRED$listSingleCases[which(perpopRED$PopName%in%ListMatches)]<-"Match"
perpopRED$listSingleCases[which(perpopRED$PopName%in%intersect(ListMisaligned, ListMatches))]<-"MatchBUTMisaligned"
perpopRED$listSingleCases[which(perpopRED$PopName%in%DRIFTONI)]<-"Drifted"
#**************************************************
### table of cases mismatch enclaves
# Supplementary Table S2
EnclavesMismatch<-perpopRED[grep("MISMATCH",perpopRED$EnclavesMismatch),]
perpopRED$DIFFFamilyClosestFAM<-perpopRED$glottolog.NAME[match(perpopRED$DIFFFamilyClosestpop,perpopRED$PopName)]
colnameschoice<-c("PopName","glottolog.NAME", "geodistSameFamily","geodistDIFFFamily","DIFFFamilyClosestpop", "DIFFFamilyClosestFAM")
EnclavesMismatch<-EnclavesMismatch[,colnameschoice]
EnclavesMismatch$MisalignedMedian<-NA
EnclavesMismatch$MisalignedMedian<-perpopRED$MedDiff[match(EnclavesMismatch$PopName,perpopRED$PopName)]
EnclavesMismatch$MisalignedMedian<-EnclavesMismatch$MisalignedMedian<0
EnclavesMismatch$MisalignedLowerCI<-NA
EnclavesMismatch$MisalignedLowerCI<-perpopRED$MedDiffCIlower[match(EnclavesMismatch$PopName,perpopRED$PopName)]
EnclavesMismatch$MisalignedLowerCI<-EnclavesMismatch$MisalignedLowerCI<0
EnclavesMismatch$MisalignedUpperCI<-NA
EnclavesMismatch$MisalignedUpperCI<-perpopRED$MedDiffCIupper[match(EnclavesMismatch$PopName,perpopRED$PopName)]
EnclavesMismatch$MisalignedUpperCI<-EnclavesMismatch$MisalignedUpperCI<0
# write.table(EnclavesMismatch[order(EnclavesMismatch$geodistDIFFFamily),],"tableSupplEnclaves2022.txt", sep="\t", row.names = F, quote=F)
#******************************************
## figure distribution FST SAME OR DIFFERENT FAMILY for each population
## Figure 1C, case studies
#******************************************
# subset case studies
# FstListREDinfo_noDuplicateNeighbors<-FstListinfo[-which(FstListinfo$GEOdist<10&FstListinfo$glottocodeBase1==FstListinfo$glottocodeBase2),]
# FstListREDinfo_noDuplicateNeighborsNoDrift<-FstListREDinfo_noDuplicateNeighbors[-c(which(FstListREDinfo_noDuplicateNeighbors$Pop2%in%DRIFTONI), which(FstListREDinfo_noDuplicateNeighbors$Pop1%in%DRIFTONI)),]
casestudies<-c( "Kalmyk", "Azeri_Azerbajan")
meltoni<-NA
for (i in 1:length(casestudies)){
TARGET<-casestudies[i]
meltini<-FstListREDinfo_noDuplicateNeighborsNoDrift[which(FstListREDinfo_noDuplicateNeighborsNoDrift$Pop1==TARGET),]
meltiniSAME<-meltini[which(meltini$SameFamily=="YES"),]
maxgeofam<-max(meltiniSAME$GEOdist, na.rm = T)
meltini<-meltini[which(meltini$GEOdist<=maxgeofam),]
meltini$TARGET<-TARGET
meltoni<-rbind(meltoni, meltini)
}
meltoni<-meltoni[-1,]
meltoni$TARGET<-factor(meltoni$TARGET, levels=casestudies)
ggg<-ggplot(meltoni,aes(y=FST, x=SameFamily,color=SameFamily))
ggg+ geom_jitter(shape=16, position=position_jitter(0.2))+
geom_violin(trim=FALSE, alpha=0.4)+
stat_summary(fun.y= median,
geom="pointrange", color="gold") +
theme_minimal()+
scale_y_log10()+
scale_color_manual(values=c(blueNO,darkredYES))+
guides(color=FALSE)+
facet_wrap(~TARGET,nrow = 1)
# scales="free")
ggsave("Fig1C_caseStudyMisalignedVIOLIN_YESorNO_2022.pdf", height=3,width=5.5, useDingbats=FALSE)
###
#*#******************************************
#* #*#******************************************
#* #*#******************************************
#*
#*
######################################################################
########################################################
##############
# language family comparisons
# geographic distance and smooth or linear regression
## testing if SAME FAMILY has an effect on Fst given GeoDist
############################
############################
#
# FstListREDinfo_noDuplicateNeighbors<-FstListinfo[-which(FstListinfo$GEOdist<10&FstListinfo$glottocodeBase1==FstListinfo$glottocodeBase2),]
#
# FstListREDinfo_noDuplicateNeighborsNoDrift<-FstListREDinfo_noDuplicateNeighbors[-c
# (which(FstListREDinfo_noDuplicateNeighbors$Pop2%in%DRIFTONI),
# which(FstListREDinfo_noDuplicateNeighbors$Pop1%in%DRIFTONI)),]
#*********************************************
### FIGURE 2
#*********************************************
#*
#*
### Within/between family comparisons
# original script from Damián Blasi, adapted by C. Barbieri
#
# FIGURE 2
#
library(tidyverse)
#large_families<-MainFamilies[-13] # exclude Tupi
large_families<-c( "Atlantic-Congo" , "Indo-European", "Sino-Tibetan" , "Mongolic-Khitan",
"Afro-Asiatic", "Turkic" , "Austronesian" , "Uralic") # families above 8 pops
pops_for_test<-FstListREDinfo_noDuplicateNeighborsNoDrift
pops_for_test$FAMILY<-factor(pops_for_test$FAMILY, levels=large_families) # force the order when doing facet wrap in the figure
pops_for_test$listSingleCasesPOP1<-perpopRED$listSingleCases[match(pops_for_test$Pop1, perpopRED$PopName)]
pops_for_test$listSingleCasesPOP1[which(pops_for_test$listSingleCasesPOP1=="Match")]<-"ND"
pops_for_test$listSingleCasesPOP1[which(pops_for_test$listSingleCasesPOP1=="MatchBUTMisaligned")]<-"ND"
pops_for_test$listSingleCasesPOP1[which(pops_for_test$listSingleCasesPOP1=="ND")]<-NA
pops_for_test$listSingleCasesPOP1[which(pops_for_test$SameFamily=="NO")]<-NA # do not want to plot mismatches in different lang family comparisons
FAM<- large_families
for(f in FAM) {
pops_for_test[[f]]<-(pops_for_test$family1==f)|(pops_for_test$family2==f)
threshold_geo<-max(pops_for_test$GEOdist[pops_for_test$FAMILY==f],
na.rm = T)
if(threshold_geo<500){
threshold_geo<-500
}
print(f)
print(threshold_geo)
pops_for_test[[f]]<-pops_for_test[[f]]*(pops_for_test$GEOdist<=threshold_geo)
pops_for_test[[f]]<-sapply(pops_for_test[[f]],function(x) ifelse(is.na(x),FALSE,x))
}
family_distances<-pivot_longer(pops_for_test,
cols = FAM,
names_to = "Family_plot",
values_to = "Include") %>%
filter(Include==TRUE)
#### add the within comparison symbols for listSingleCasesPOP1, flagging mismatches
family_distances$Family_plot<-factor(family_distances$Family_plot, levels=large_families) # force the order when doing facet wrap in the figure
family_distances %>%
ggplot(aes(y=FstLinear,x=GEOdist))+
geom_point(aes(fill=SameFamily, size=SameFamily),shape=21, alpha=0.3, stroke=0)+
geom_point(aes(fill=SameFamily, shape=listSingleCasesPOP1),alpha=0.3,size=3, stroke=0.3)+
geom_smooth(aes(group=SameFamily),se=F, color="white", size=2.5)+
geom_smooth(aes(color=SameFamily),se=F)+
geom_smooth(color="#FBB13C",linetype = "dotdash",alpha=0.4, size=1, method = lm)+
facet_wrap(~Family_plot,
scales="free",
nrow=length(FAM)/4)+
theme_minimal()+
scale_y_sqrt()+
scale_fill_manual(values=c("#7FDBEB","#E45B5B"))+
scale_color_manual(values=c(blueNO,darkredYES))+
scale_shape_manual(" ",values = c(25,23,22))+
scale_size_manual(values = c(1,2))+
theme(legend.position = "bottom", axis.text=element_text(size=7),strip.text = element_text(size = 12))+
xlab("Geographic Distance - km") +
ylab("Linear FST")
ggsave("Fig2_smoothregressionPlotwithLinear_highlightMismatches_2022.pdf", useDingbats=FALSE, height = 6, width = 10)
ggsave("Fig2_smoothregressionPlotwithLinear_highlightMismatches_2022.png", height = 5, width = 10)
# ***************************************************
#### SUPPLEMENTARY FIGURES
# ***************************************************
# ***************************************************
# ***************************************************
########################################################
# Figure S8 supplementary - comparison of difference of medians and CI, colored per language family
########################################################
MainFamilies<-unlist(labels(Lfamil[which(Lfamil>4)])) # minimum 5 populations per Lang Family
perpopREDfamily<-perpopRED[which(perpopRED$glottolog.NAME %in% MainFamilies),]
perpopREDfamilynoDrift<-perpopREDfamily[-which(perpopREDfamily$listSingleCases=="Drifted"),]
perpopREDEnclaves<-perpopRED[which(perpopRED$EnclavesMismatch%in%c("MATCH", "MISMATCH","secondaryMISMATCH_FSTzeroDiffFamily")),]
perpopREDEnclaves<-perpopRED[which(perpopRED$EnclavesMismatch%in%c("MATCH", "MISMATCH","secondaryMISMATCH_FSTzeroDiffFamily")),]
perpopRED$enclavesAgain<-NA
perpopRED$enclavesAgain[which(perpopRED$EnclavesMismatch=="MATCH")]<-"Match"
perpopRED$enclavesAgain[which(perpopRED$EnclavesMismatch=="MISMATCH")]<-"GeneticEnclave"
perpopRED$enclavesAgain[which(perpopRED$EnclavesMismatch=="secondaryMISMATCH_FSTzeroDiffFamily")]<-"LinguisticEnclave"
perpopREDnoDrift<-perpopRED[-which(perpopRED$listSingleCases=="Drifted"),]
library(ggrepel)
# panel A, color code language families all together
fst1<-ggplot(perpopREDnoDrift,aes(MedDiff,MedDiffCI, color=MainFamilies))+
geom_point(alpha=0.5, size=3,show.legend = FALSE)+
#geom_text_repel(aes(label=PopName, color=MainFamilies), size=0.5)+
scale_color_manual(values = colorchoice)+
theme_light() +
xlab("Difference Median FST between - within") +
ylab("Confidence Interval median difference") +
geom_vline(xintercept=0)+
scale_y_log10()
#ggsave("DifferenceMedianLogScale.pdf", height = 12, width = 14, useDingbats=FALSE)
# panel C, separate language families facet wrap
fst2<-ggplot(perpopREDfamilynoDrift ,aes(MedDiff,MedDiffCI, color=MainFamilies))+
geom_point(alpha=0.5, size=3,show.legend = FALSE)+
geom_text_repel(aes(label=PopName, color=MainFamilies), size=0.5, show.legend = FALSE)+
scale_color_manual(values = colorchoice)+
theme_light()+ xlab("Difference Median FST between - within") +
ylab("Confidence Interval median difference") +
geom_vline(xintercept=0)+
scale_y_log10()+
facet_wrap(~MainFamilies,ncol=6)
#ggsave("DifferenceMedianLogScaleMajorLangFam.pdf", height = 12, width = 14, useDingbats=FALSE)
# panel B, color code previous enclaves and matches
fst3<-ggplot(perpopREDnoDrift,aes(MedDiff,MedDiffCI, color=enclavesAgain))+
geom_point(alpha=0.5, size=2)+
geom_text_repel(data=perpopREDnoDrift[!is.na(perpopREDnoDrift$enclavesAgain),],
aes(MedDiff,MedDiffCI,label=PopName, color=enclavesAgain), size=1.2)+
# scale_color_manual(values = colorchoice2)+
theme_light() +
xlab("Difference Median FST between - within") +
ylab("Confidence Interval median difference") +
geom_vline(xintercept=0)+
theme(legend.title = element_blank())+
scale_y_log10()
#ggsave("DifferenceMedianwithTextENCLAVES.pdf", height = 12, width = 14, useDingbats=FALSE)
library(patchwork)
patchwork<-(fst1 | fst3) / fst2
patchwork + plot_annotation(tag_levels = 'A')
ggsave("DifferenceMedianLogScaleGLOBALandMajorLangFam_3plots_2022.pdf", height = 10, width = 12, useDingbats=FALSE)
ggsave("DifferenceMedianLogScaleGLOBALandMajorLangFam_3plots_2022.png", height = 10, width = 12, dpi = 150)
########
## the map with singular mismatches of linguistic and genetic migrants, misaligned and drifted
# Figure S10
########
library(ggrepel)
perpopRED2<-perpopRED[!is.na(perpopRED$lat),]
perpopREDSHIFTMAP<-MoveDataCoord(perpopRED2)
perpopREDSHIFTMAPinterest<-perpopREDSHIFTMAP[-which(perpopREDSHIFTMAP$listSingleCases=="ND"),]
#perpopREDSHIFTMAPmismatch<-perpopREDSHIFTMAPinterest[which(perpopREDSHIFTMAPinterest$singlepops%in%c("Misalligned","Enclave")),]
perpopREDSHIFTMAPnodata<-perpopREDSHIFTMAP[which(perpopREDSHIFTMAP$EnclavesMismatch=="ZeroSameFamilyNeighbors"),]
#perpopREDSHIFTMAPinterest<-perpopREDSHIFTMAPinterest[-which(perpopREDSHIFTMAPinterest$PopName%in% enclavesByMistake),]
#perpopREDSHIFTMAPmismatch<-perpopREDSHIFTMAPinterest[which(perpopREDSHIFTMAPinterest$EnclavesMismatch%in%c("MISMATCH","secondaryMISMATCH_FSTzeroDiffFamily")),]
### variant with language family color
# and symbols for single cases
base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color="black",shape=3,size=0.5)+
theme(legend.position="bottom",legend.title = element_blank())+
geom_point(data = perpopREDSHIFTMAP[which(perpopREDSHIFTMAP$MainFamilies%in%MainFamilies),],
aes(x = lon.1, y = lat.1, color=MainFamilies),
size = 2, alpha=0.6)+
geom_point(data=perpopREDSHIFTMAPinterest, aes(x = lon.1, y = lat.1,
shape=listSingleCases),alpha=0.5, size=2.5)+
# geom_point(data=perpopREDSHIFTMAPnodata, aes(x = lon.1, y = lat.1),
# color="ghostwhite", shape= 13,alpha=0.8)+
scale_shape_manual(values = c(11,6,5,8,7,0))+
scale_color_manual(values=colorchoice, labels = MainFamilies2$names)
ggsave("Fig_s10_WholeGelatoMap_Pacific_LittleCrossesMajorFamilies_SingleCases_rightSymbols_2022.pdf", useDingbats=FALSE, width = 14, height = 8, dpi = 300, units = "in")
ggsave("Fig_s10_WholeGelatoMap_Pacific_LittleCrossesMajorFamilies_SingleCases_rightSymbols_2022.png", width = 14, height = 8, dpi = 300, units = "in")
# ***************************************************
## Figure S3
library(plyr)
threshold<-c(500,1000,1500,2000,2500,3000)
blockneighborthresholdGeo<-c()
for (i in 1:length(threshold)){
testino2<-ddply(FstListinfo, "Pop1", function(x) length(which(x$GEOdist<threshold[i]&x$FAMILY=="DIVERSE"))) # no continental filter
colnames(testino2)<-c("PopName","GeoCloseUnrelated1")
testinomerged<-testino2
testinomerged$GeoCloseUnrelated<-testinomerged$GeoCloseUnrelated1/2
testinomerged$threshold<-threshold[i]
blockneighborthresholdGeo<-rbind(blockneighborthresholdGeo,testinomerged)
}
blockneighborthresholdGeoINFO<-merge(perpopRED,blockneighborthresholdGeo)
# frequencies at least one neighbor from diff family
freqOneneighbor<-c()
for (i in 1:length(threshold)){
thresholdtemp<-threshold[i]
popsNumberofNeighbors<-ddply(FstListinfo, "Pop1", function(x) length(which(x$GEOdist<thresholdtemp&x$FAMILY=="DIVERSE"))) # no continental filter
freqOneneighbor[i]<-length(which(popsNumberofNeighbors$V1>0))/nrow(perpopRED)
}
valuestop<-round(freqOneneighbor*100)
[1] 57 85 93 98 99 100 # add manually on the violin plot figure
valuestop<-paste0(valuestop, "%")
# violin plots with number of populations from a different language family
# fig S3B
blockneighborthresholdGeoINFO$threshold<-as.character(blockneighborthresholdGeoINFO$threshold)
blockneighborthresholdGeoINFO$threshold[which(blockneighborthresholdGeoINFO$threshold=="500")]<-" 500"
ga<-ggplot(blockneighborthresholdGeoINFO,aes(x=threshold,y=GeoCloseUnrelated1))
S3B <-ga+ geom_jitter(shape=16, position=position_jitter(0.2))+
geom_violin(trim=FALSE, alpha=0.4)+
stat_summary(fun.data="mean_sdl",
geom="pointrange", color="orange")+
theme_minimal()+
theme(axis.title.y = element_text(size = 8)) +
ylim(0,150)+xlab("Geographic radius in km")+
ylab("Number of populations from a different language family") +
annotate("text", x = 1:6, y = 150, label = valuestop)
# ***************************************************
# evaluate the incidence of neighbors from a different L famiy for each population
# count how many populations from different L families in a radius of 1000 km in the same continent
popsNumberofNeighbors<-ddply(FstListinfo, "Pop1", function(x) length(which(x$GEOdist<1000&x$FAMILY=="DIVERSE"))) # no continental filter
perpopRED$nNeighborsDiffFamily<-popsNumberofNeighbors
colnames(popsNumberofNeighbors)<-c("PopName","GeoCloseUnrelated")
perpopRED3<-merge(perpopRED,popsNumberofNeighbors)
perpopRED4<-perpopRED3[-which(perpopRED3$GeoCloseUnrelated==0),]
perpopRED4_SHIFTMAP<-MoveDataCoord(perpopRED4)
# plot on a map the density of unrelated pop geographically close
# fig S3A
S3A <- base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color = "black",
size = 0.5, shape=3)+
geom_point(data=perpopRED4_SHIFTMAP, aes(x = lon.1, y = lat.1,color=as.numeric(GeoCloseUnrelated)), alpha=0.5, size=2)+
# ggtitle("number of neighbors from a different language family")+
scale_color_gradient(low = "blue", high = "red",name="") +
xlab("") + ylab("")
# theme(legend.title = element_blank())
# ggsave("DensityPopulationsWithDifferentLFamilyNeighbors_noContinentFilter_map_PacificCenter_1000km_2021_.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
# count how many LANGUAGE FAMILIES in a radius of 1000 km in the same continent
popsNumberofNeighborFamilies<-ddply(FstListinfo, "Pop1", function(x) length(unique(x[which(x$GEOdist<1000&x$FAMILY=="DIVERSE"),]$family2))) # no continental filter
perpopRED$nDiffFamilies1000km<-popsNumberofNeighbors
colnames(popsNumberofNeighborFamilies)<-c("PopName","GeoCloseUnrelatedFamilies")
perpopRED3<-merge(perpopRED,popsNumberofNeighborFamilies)
perpopRED4<-perpopRED3[-which(perpopRED3$GeoCloseUnrelatedFamilies==0),]
perpopRED4_SHIFTMAP<-MoveDataCoord(perpopRED4)
# plot on a map the density of unrelated Language Families geographically close
# fig S3C
S3C <- base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color = "black",
size = 0.5, shape=3)+
geom_point(data=perpopRED4_SHIFTMAP, aes(x = lon.1, y = lat.1,color=as.numeric(GeoCloseUnrelatedFamilies)), alpha=0.5, size=2)+
# ggtitle("number of neighbors from a different language family")+
scale_color_gradient(low = "blue", high = "red",name="") +
xlab("") + ylab("")
# ggsave("DensityDifferentLFamilyNeighbors_noContinentFilter_map_PacificCenter_1000km_2021.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
## assemble
library(patchwork)
layout <- "
AAAA
AAAA
#BB#
CCCC
CCCC
"
S3A + S3B + S3C +
plot_layout(design = layout)+ plot_annotation(tag_levels = 'A')
ggsave("Fig_s03_evaluateIncidenceLanguageFamilies2022.pdf", height = 12, width = 10, useDingbats=FALSE)
ggsave("Fig_s03_evaluateIncidenceLanguageFamilies2022.png", height = 12, width = 10, dpi = 150)
#******************************************
# control the distribution of divergence time for each macro region
# fig S5
#******************************************
perpopREDNe<-perpopRED[which(perpopRED$USEforNe_calculation=="YES"),]
possiblepopswithNE<-perpopREDNe$PopName
meltFstREDinfoYESne<-FstListinfo[which(FstListinfo$Pop1%in%possiblepopswithNE),]
meltFstREDinfoYESne<-meltFstREDinfoYESne[which(meltFstREDinfoYESne$Pop2%in%possiblepopswithNE),]
meltFstGlotto_infowithinREgionTMRCA<-meltFstREDinfoYESne[which(meltFstREDinfoYESne$region1==meltFstREDinfoYESne$region2),]
meltFstGlotto_infowithinREgionTMRCA<-meltFstGlotto_infowithinREgionTMRCA[which(meltFstGlotto_infowithinREgionTMRCA$case=="single" ),]
## exclude drifted pops or the Fst averages will be higher than normal
#DRIFTONI<-perpopRED[which(perpopRED$medianFSTregion>0.1&perpopRED$medianFST>0.1),]$PopName
meltFstGlotto_infowithinREgionTMRCA<-meltFstGlotto_infowithinREgionTMRCA[-c(which(meltFstGlotto_infowithinREgionTMRCA$Pop2%in%DRIFTONI)),]
meltFstGlotto_infowithinREgionTMRCA<-meltFstGlotto_infowithinREgionTMRCA[-c(which(meltFstGlotto_infowithinREgionTMRCA$Pop1%in%DRIFTONI)),]
meltFstGlotto_infowithinREgionTMRCA$TMRCA_doubleNe<-as.numeric(meltFstGlotto_infowithinREgionTMRCA$TMRCA_doubleNe)
ggplot(meltFstGlotto_infowithinREgionTMRCA, aes(region1, TMRCA_doubleNe))+
geom_jitter(shape=16, position=position_jitter(0.2))+
geom_violin(trim=FALSE, alpha=0.4)+
stat_summary(fun.data="mean_sdl",
geom="pointrange", color="orange")+
ylim(0,70000)+xlab("")+ylab("population divergence time, years ago")+
theme_light()
ggsave("FigS05_distribTMRCA_ContinentsViolin2022.pdf", useDingbats=FALSE, width=6, height = 4)
#******************************************
#******************************************
#******************************************
#******************************************
# ANALYSIS PERCENTILES
#******************************************
# OVERVIEW OF MISMATCHES WITH CLOSE FST DISTANCES
#******************************************
#******************************************
#* Figure S6
# ------------------------------------------------------
# CONTINENT MEDIAN FST and a range of percentile threshold values
## adjust dataset without very Drifted and within and between regions
FstListGlotto_infowithinREgion<-FstListinfo[which(FstListinfo$region1==FstListinfo$region2),]
## exclude drifted pops or the Fst averages will be higher than normal
#DRIFTONI<-perpopRED[which(perpopRED$medianFSTregion>0.1&perpopRED$medianFST>0.1),]$PopName
FstListGlotto_infowithinREgionNoDrif<-FstListGlotto_infowithinREgion[-c(which(FstListGlotto_infowithinREgion$Pop2%in%DRIFTONI), which(FstListGlotto_infowithinREgion$Pop1%in%DRIFTONI)),]
FstListGlottoIBD_infoNoDrift<-FstListinfo[-c(which(FstListinfo$Pop2%in%DRIFTONI), which(FstListinfo$Pop1%in%DRIFTONI)),]
# set a series of percentiles up to 0.5
percentiles<-seq(from = .002, to = .5, by = .002)
#percentilesCoarse<-seq(from = .005, to = .5, by = .005)
# calculate thresholds per continents
library(plyr)
medianGEO<-ddply(FstListGlotto_infowithinREgionNoDrif, "region1", function(x) median(x$FST))
valuespercentileRegions<-data.frame(row.names =medianGEO$region1)
for (i in 1:length(percentiles)){
percTarget<-percentiles[i]
temp<-ddply(FstListGlotto_infowithinREgionNoDrif, "region1", function(x) quantile(x$FST, percTarget))
valuespercentileRegions[,i]<-temp[,2]
#colnames(valuespercentileRegions[i])<-colnames(temp)[2]
}
colnames(valuespercentileRegions)<-percentiles
medianGEO<-cbind(medianGEO,valuespercentileRegions)
colnames(medianGEO)[2]<-"medianRegion"
aggiuntaWorld<-c() # the list of percentile threshold on global distribution
for (i in 1:length(percentiles)){
percTarget<-percentiles[i]
aggiuntaWorld[i]<-quantile(FstListGlottoIBD_infoNoDrift$FST, percTarget)
}
aggiuntaWorldline<-c("WORLD", median(FstListGlottoIBD_infoNoDrift$FST),aggiuntaWorld)
medianGEO<-rbind(medianGEO,aggiuntaWorldline)
# assign the lowest percentile to each pair fst
FstListinfo$percentileFST<-NA
for (i in 1:nrow(FstListinfo)){
if(FstListinfo$region1[i]!=FstListinfo$region2[i] ){
FstListinfo$percentileFST[i]<- percentiles[which(FstListinfo$FST[i]<aggiuntaWorld)][1]
}
else {
reftemp<- valuespercentileRegions[which( medianGEO$region1== FstListinfo$region2[i]),] # list of percentiles corresponding to the continent of the two populations
FstListinfo$percentileFST[i]<- percentiles[which(FstListinfo$FST[i]<reftemp)][1]
}
}
# prepare plottable file for pairwise connections
betweenfamilSMALLgeoplottabile1<-FstListinfo
betweenfamilSMALLgeoplottabile1$index<-c(1:nrow(FstListinfo))
betweenfamilSMALLgeoplottabile1<-betweenfamilSMALLgeoplottabile1[,-which(colnames(betweenfamilSMALLgeoplottabile1)%in%c("lat2","lon2"))]
betweenfamilSMALLgeoplottabile2<-FstListinfo
betweenfamilSMALLgeoplottabile2$index<-c(1:nrow(FstListinfo))
betweenfamilSMALLgeoplottabile2<-betweenfamilSMALLgeoplottabile2[,-which(colnames(betweenfamilSMALLgeoplottabile2)%in%c("lat1","lon1"))]
betweenfamilSMALLgeoplottabile<-rbind(betweenfamilSMALLgeoplottabile1,setNames(betweenfamilSMALLgeoplottabile2,names(betweenfamilSMALLgeoplottabile1)))
betweenfamilSMALLgeoplottabile$lat=as.numeric(as.character(betweenfamilSMALLgeoplottabile$lat1))
betweenfamilSMALLgeoplottabile$lon=as.numeric(as.character(betweenfamilSMALLgeoplottabile$lon1))
betweenfamilSMALLgeoplottabile$index=as.numeric(as.character(betweenfamilSMALLgeoplottabile$index))
betweenfamilSMALLgeoplottabile<-betweenfamilSMALLgeoplottabile[!is.na(betweenfamilSMALLgeoplottabile$lat),]
betweenfamilSMALLgeoplottabile<-betweenfamilSMALLgeoplottabile[!is.na(betweenfamilSMALLgeoplottabile$percentileFST),]
#betweenfamilSMALLgeoplottabile$percentileFST<-1-betweenfamilSMALLgeoplottabile$percentileFST
betweenfamilSMALLgeoplottabileSHIFTMAP<-MoveDataCoord(betweenfamilSMALLgeoplottabile)
# one map with increasing color connections for increasing percentile
# from percentile 0.2 to 0.01
betweenfamilSMALLgeoplottabileSHIFTMAP2<-betweenfamilSMALLgeoplottabileSHIFTMAP[which(betweenfamilSMALLgeoplottabileSHIFTMAP$percentileFST<0.1),]
betweenfamilSMALLgeoplottabileSHIFTMAP2<-betweenfamilSMALLgeoplottabileSHIFTMAP2[order(betweenfamilSMALLgeoplottabileSHIFTMAP2$percentileFST, decreasing = T),]
# only different L family pairs
betweenfamilSMALLgeoplottabileSHIFTMAP3<-betweenfamilSMALLgeoplottabileSHIFTMAP2[which(betweenfamilSMALLgeoplottabileSHIFTMAP2$FAMILY=="DIVERSE"),]
fig6a<-base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color = "black",
size = 0.5, shape=3)+
geom_path(data=betweenfamilSMALLgeoplottabileSHIFTMAP3, aes(x = lon.1, y = lat.1,
group=index, alpha=percentileFST, size=percentileFST, color=percentileFST))+
scale_alpha(range = c(0.2,0.1))+
scale_size(range = c(0.5,.1))+
scale_colour_gradient2(midpoint=0.1, low="darkred", mid="darkorange",
high="white", name="Percentile FST distribution")+
#ggtitle("weight of mismatches according to FST percentile distribution")+
xlab("") + ylab("") +
guides(alpha=FALSE, size=FALSE)
### Supplementary Figure S6A
ggsave("MapPairMismatchesPercentileDistribution_until10percentile_2022_.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
# ***************************************************
# sensitivity test with different geo thresholds
# Figure S2
### all the close FST distances on a map
base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color = "black",
size = 0.5, shape=3)+
geom_path(data=betweenfamilSMALLgeoplottabileSHIFTMAP2, aes(x = lon.1, y = lat.1,
group=index, alpha=percentileFST, color=percentileFST))+
scale_alpha(range = c(0.2,0.01))+
scale_size(range = c(0.5,.01))+
scale_colour_gradient2(midpoint=0.05, low="purple", mid="yellow",
high="white", name="Percentile FST distribution")+
xlab("") + ylab("") +
guides(alpha=FALSE, size=FALSE)
ggsave("MapPairFSTPercentileDistribution_2022.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
##-------------------------------------
# figure area proportion of linguistically unrelated in close FSTs
# supplementary Fig S6B
##-------------------------------------
#FstListREDinfo_diverse<-FstListREDinfo[which(FstListREDinfo$FAMILY=="DIVERSE"),]
FstListGlotto_infowithinREgion<-FstListinfo[which(FstListinfo$region1==FstListinfo$region2),]
FstListREDinfo_BetweenRegion<-FstListinfo[which(FstListinfo$region1!=FstListinfo$region2),]
numberTotalSmallFST<-c()
numberTotalSmallFSTDIVERSE<-c()
percentageMismatchesinTotalSmallFST<-c()
meltFstGlottoIBD_infoWithPercentageMismatch<-data.frame(row.names = FstListinfo$popslistemp)
for (j in 1:length(percentiles)){
mismatchlist<-c()
percTarget<-percentiles[j]
for (i in 1:5){ # the five macro continents
mismatchlist1<-FstListGlotto_infowithinREgion[which(FstListGlotto_infowithinREgion$region1==medianGEO[i,1]),] # select continent
thresholdtemp<-as.numeric(medianGEO[i,which(colnames(medianGEO)==percTarget)]) # select percentile value fst
mismatchlist1<-mismatchlist1[which(mismatchlist1$FST<= thresholdtemp),]
mismatchlist<-rbind(mismatchlist,mismatchlist1)
}
thresholdtempworld<-as.numeric(medianGEO[6,which(colnames(medianGEO)==percTarget)])
mismatchlistBEtweenregion<-FstListREDinfo_BetweenRegion[which(FstListREDinfo_BetweenRegion$FST<= thresholdtempworld),] # use the target quantile all over the world Fst WORLD
TheMismatches<-rbind(mismatchlist, mismatchlistBEtweenregion)
numberTotalSmallFST[j]<-nrow(TheMismatches) # number of pairs within FST percentile
numberTotalSmallFSTDIVERSE[j]<-length(which(TheMismatches$FAMILY=="DIVERSE")) # number of pairs in mismatch
percentageMismatchesinTotalSmallFST[j]<-length(which(TheMismatches$FAMILY=="DIVERSE"))/nrow(TheMismatches)
meltFstGlottoIBD_infoWithPercentageMismatch[,j]<-NA
meltFstGlottoIBD_infoWithPercentageMismatch[TheMismatches$popslistemp,j]<-percTarget
}
colnames(meltFstGlottoIBD_infoWithPercentageMismatch)<-percentiles
listperpercentileNumberCases<-rbind(numberTotalSmallFST,numberTotalSmallFSTDIVERSE,percentageMismatchesinTotalSmallFST)
colnames(listperpercentileNumberCases)<-percentiles
# count the cases below each percentile
f<-function(x, output){sort(x)[1]}
FstListinfo$percentileFSTdoublecheck<-apply(meltFstGlottoIBD_infoWithPercentageMismatch,
1, f)
plot(FstListinfo$percentileFSTdoublecheck, FstListinfo$percentileFST) # check if the two slightly different method give the same percentiles
plotproportion<-data.frame(percentiles,nrow(FstListinfo)-numberTotalSmallFST,numberTotalSmallFST-numberTotalSmallFSTDIVERSE,numberTotalSmallFSTDIVERSE)
colnames(plotproportion)<-c("percentiles","pairsOutside","pairsInsideSameFamily","pairsInsideDiffFamily")
fig6B<-ggplot(plotproportion,aes(x=as.numeric(percentiles), y=percentageMismatchesinTotalSmallFST, color=as.numeric(percentiles)))+
geom_segment(aes(xend=as.numeric(percentiles), yend=0, color=as.numeric(percentiles)), alpha=0.9)+
geom_line(size=1)+
# geom_area(aes(x=as.numeric(percentiles), y=percentageMismatchesinTotalSmallFST), alpha=0.5, fill="darkorange", color="darkorange")+
#ggtitle("proportion of couples from Diff L Families over number of pairs genetically close - FST percentile distrib")+
labs(x="Percentile global Fst distribution", y="proportion of pairs from Different Language Families")+
#geom_vline(xintercept = 0.2, color="darkorange", size=1)+
scale_colour_gradient2(midpoint=0.2, low="darkred", mid="darkorange",