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AnalysisMaMi.r
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#******************************************
#******************************************
### analysis Paper MaMi
# Chiara BArbieri 30 March 2021
#******************************************
#*
#*
#*
#write.table(FstListREDinfo, "/Users/chiarabarbieri/Library/Mobile Documents/com~apple~CloudDocs/GeLaTo/FstListREDinfo2021.txt", sep="\t", row.names = F, quote=F)
#write.table(perpopRED, "/Users/chiarabarbieri/Library/Mobile Documents/com~apple~CloudDocs/GeLaTo/perpopREDMaMi2021.txt", row.names = F, sep = "\t", quote = F)
#source("/Users/chiarabarbieri/Library/Mobile Documents/com~apple~CloudDocs/ggworld.R")
# setwd("/Users/chiarabarbieri/switchdrive/GeLaTo")
### read the two main files
# list of 404 populations :
#perpopRED<-read.table("/Users/chiarabarbieri/Library/Mobile Documents/com~apple~CloudDocs/GeLaTo/perpopREDMaMi2021.txt", sep = "\t", header=T, as.is=T)
perpopRED<-read.table("/Users/chiarabarbieri/Library/Mobile Documents/com~apple~CloudDocs/GeLaTo/perPopMarchMami2.txt", sep = "\t", header=T, as.is=T)
# list of pairwise comparisons :
FstListREDinfo<-read.table("/Users/chiarabarbieri/Library/Mobile Documents/com~apple~CloudDocs/GeLaTo/FstListREDinfo2021.txt",header = T, as.is=T, sep="\t")
# color palette for major language families
Lfamil<-table(perpopRED$glottolog.NAME)
MainFamilies<-unlist(labels(Lfamil[which(Lfamil>4)])) # minimum 5 populations per Lang Family
#MainFamilies<-MainFamilies[-which(MainFamilies=="LI")] # exclude the Language Isolates LI
perpopREDfamily<-perpopRED[which(perpopRED$glottolog.NAME %in% MainFamilies),]
colorchoice<-c( "darkorange4" ,"#A6CEE3" , "#33A02C" , "#A6D854" , "#377EB8" , "#E31A1C" , "#FFD92F" , "#FF7F00" , "#666666" ,
"cyan4" , "#BC80BD" , "#FED9A6" , "tan3" , "#6A3D9A" , "deeppink" )
MainFamilies2<-as.data.frame(MainFamilies)
MainFamilies2$COLOR<-colorchoice
library(ggplot2)
#******************************************
### MAP
#******************************************
library(maps)
library('maps')
library('geosphere')
library(rworldmap)
source("/Users/chiarabarbieri/Library/Mobile Documents/com~apple~CloudDocs/ggworld.R")
perpopRED$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
#******************************************
#*
base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color="black",shape=3,size=0.5)+
theme(legend.position="bottom")+
geom_point(data = perpopREDSHIFTMAP[which(perpopREDSHIFTMAP$MainFamilies%in%MainFamilies),],
aes(x = lon.1, y = lat.1, color=MainFamilies),
size = 2, alpha=0.5)+
scale_color_manual(values=colorchoice)
ggsave("WholeGelatoMap_Pacific_LittleCrossesMajorFamilies.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
#******************************************
#*#******************************************
#* genetic and linguistic enclaves
#*#******************************************
# ***************************************************
# evaluate the incidence of neighbors from the SAME AND THE different L famiy for each population
# closer neighbor from same and different L family
perpopRED$hasOtherMatches<-"YES"
perpopRED$closeFstSameFamily<-NA
perpopRED$geodistSameFamily<-NA
perpopRED$closeFstDIFFFamily<-NA
perpopRED$geodistDIFFFamily<-NA
perpopRED$DIFFFamilyClosestpop<-NA
perpopRED$SameFamilyMostDistantClosestFst<-NA
# how far the same family is gen closer than other families?
for (i in 1:nrow(perpopRED)){
targetpop<-perpopRED$PopName[i]
temp<-FstListREDinfo[which(FstListREDinfo$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
}
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$Mismatch3a<-NA
#perpopRED$Mismatchsingular2[which(perpopRED$closeFstSameFamil==0&perpopRED$geodistSameFamily>100&perpopRED$closeFstDIFFFamily!=0)] <- "secondaryMATCH_FSTzero_geodist100"
perpopRED$Mismatch3a[which(perpopRED$closeFstDIFFFamily==0&perpopRED$closeFstSameFamil!=0)] <- "secondaryMISMATCH_FSTzeroDiffFamily"
perpopRED$Mismatch3a[which(perpopRED$proportionFST_diff_sameFamily<1&perpopRED$proportionGeoDistSameDiffFamily>1)] <- "MISMATCH"
perpopRED$Mismatch3a[which(perpopRED$proportionFST_diff_sameFamily>1&perpopRED$proportionGeoDistSameDiffFamily<1)] <- "MATCH"
perpopRED$Mismatch3a[which(perpopRED$hasOtherMatches!="YES" )]<-"ZeroSameFamilyNeighbors"
table(perpopRED$Mismatch3a)/nrow(perpopRED)
### table to visualize how many cases
#*** 2021
MATCH MISMATCH
0.128712871 0.069306931
secondaryMISMATCH_FSTzeroDiffFamily ZeroSameFamilyNeighbors
0.004950495 0.049504950
#***
#Matches
ListMatches<-perpopRED$PopName [which(perpopRED$Mismatch3a=="MATCH")]
### DRIFTONI
DRIFTONI<-perpopRED[which(perpopRED$medianFSTregion>0.1&perpopRED$medianFST>0.1),]$PopName
## genetic and linguistic enclaves to exclude from further analysis
ListEnclaves<-perpopRED$PopName [which(perpopRED$Mismatch3a%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"
### manual screening
#enclavesByMistake<-c("Yoruba" ,"Nama", "Han-NChina" , "Bengali", "Evenk_FarEast") # these pops are not real enclaves, their linguistically unrelated pair is the one driving this genetic proximity effect
#ListEnclaves<-ListEnclaves[-which(ListEnclaves%in%(enclavesByMistake))]
perpopRED$ListEnclaves<-NA
perpopRED$ListEnclaves[which(perpopRED$PopName%in%ListEnclaves)]<-"Enclave"
#*#******************************************
#*
#* potential FIGURE 1 different distribution FST closest same family and different family
#*
aa<-perpopRED[,c(1,44,46,16)]
meltperpop<-melt(aa)
aa$difference<-aa$closeFstSameFamily-aa$closeFstDIFFFamily
length(which(aa$difference>0))/(404-length(which(is.na(aa$difference)))) # only for the comparisons for which i have a same family FST
[1] 0.1822917
# 18 % of the pops who have another genetic population of the same language family do have closer FST with speakers of another language family
#**************************************************
### table of cases mismatch enclaves
mismatch3a<-perpopRED[grep("MISMATCH",perpopRED$Mismatch3a),]
perpopRED$DIFFFamilyClosestFAM<-perpopRED$glottolog.NAME[match(perpopRED$DIFFFamilyClosestpop,perpopRED$PopName)]
colnameschoice<-c("PopName","glottolog.NAME", "geodistSameFamily","geodistDIFFFamily","DIFFFamilyClosestpop", "DIFFFamilyClosestFAM")
mismatch3a<-mismatch3a[,colnameschoice]
#write.table(mismatch3a[order(mismatch3a$geodistDIFFFamily),],"tableSupplMismatches3a.txt", sep="\t", row.names = F, quote=F)
#******************************************
#*#******************************************
#*#******************************************
#*#******************************************
#*#******************************************
#*#******************************************
#* COMPARING MEDIAN FST BETWEEN AND WITHIN
# POPULATION HEURISTIC 2
#* FST distribution between and within language families MISALIGNED
#*#******************************************
#*#******************************************
## exclude neighbors who speak the same language as DUPLICATE POPS
escludiniVicini<-FstListREDinfo[which(FstListREDinfo$GEOdist<10&FstListREDinfo$glottocodeBase1==FstListREDinfo$glottocodeBase2),]
Pop1 Pop2 popslistemp Fst family1 family2 region2
7803 Ju_hoan_North San Ju_hoan_NorthSan 0.003176030 Kxa Kxa AFRICA
9447 Kove Kove_Tamuniai KoveKove_Tamuniai 0.000000000 Austronesian Austronesian OCEANIA
10395 Mamanwa Mamanwa1 MamanwaMamanwa1 0.050776000 Austronesian Austronesian SOUTHEAST_ASIA
13579 Sulka_Ganai Sulka_Watwat Sulka_GanaiSulka_Watwat 0.000000000 LI LI OCEANIA
25464 BedouinA BedouinB BedouinABedouinB 0.015446000 Afro-Asiatic Afro-Asiatic EURASIA
34834 Cusco2 Peru_Quechua Cusco2Peru_Quechua 0.000427919 Quechuan Quechuan AMERICAS
36912 Druze Palestinian DruzePalestinian 0.008987530 Afro-Asiatic Afro-Asiatic EURASIA
45009 Greek_Athens Greek_WGA Greek_AthensGreek_WGA 0.003147220 Indo-European Indo-European EURASIA
46714 GujaratiA GujaratiB GujaratiAGujaratiB 0.001633070 Indo-European Indo-European EURASIA
46731 GujaratiA GujaratiC GujaratiAGujaratiC 0.002983290 Indo-European Indo-European EURASIA
46899 GujaratiB GujaratiC GujaratiBGujaratiC 0.003168390 Indo-European Indo-European EURASIA
48305 Han-NChina Han_HGDP Han-NChinaHan_HGDP 0.002480980 Sino-Tibetan Sino-Tibetan EURASIA
49975 Hungarian1 Hungarian2 Hungarian1Hungarian2 0.000000000 Uralic Uralic EURASIA
60866 Kinh Vietnamese_North KinhVietnamese_North 0.001434990 Austroasiatic Austroasiatic SOUTHEAST_ASIA
63575 Lebanese_Christian Lebanese_Muslim Lebanese_ChristianLebanese_Muslim 0.001025050 Afro-Asiatic Afro-Asiatic EURASIA
81071 Uzbek Uzbek_Tashkent UzbekUzbek_Tashkent 0.000917217 Turkic Turkic EURASIA
## exclude DRIFTONI
## exclude drifted pops or the Fst averages will be higher than normal
DRIFTONI<-perpopRED[which(perpopRED$medianFSTregion>0.1&perpopRED$medianFST>0.1),]$PopName
FstListREDinfo_noDuplicateNeighbors<-FstListREDinfo[-which(FstListREDinfo$GEOdist<10&FstListREDinfo$glottocodeBase1==FstListREDinfo$glottocodeBase2),]
FstListREDinfo_noDuplicateNeighborsNoDrift<-FstListREDinfo_noDuplicateNeighbors[-c(which(FstListREDinfo_noDuplicateNeighbors$Pop2%in%DRIFTONI), which(FstListREDinfo_noDuplicateNeighbors$Pop1%in%DRIFTONI)),]
library(pairwiseCI)
perpopRED$MedianWithinSMALLER2<-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)]
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$PopName%in%DRIFTONI)]<-"Drifted"
###
#*#******************************************
#* #*#******************************************
#* #*#******************************************
#*
#*
########################################################
# Figure S8 supplementary - comparison of difference of medians and CI, colored per language family
########################################################
perpopREDfamily<-perpopRED[which(perpopRED$glottolog.NAME %in% MainFamilies),]
perpopREDfamilynoDrift<-perpopREDfamily[-which(perpopREDfamily$listSingleCases=="Drifted"),]
perpopREDEnclaves<-perpopRED[which(perpopRED$Mismatch3a%in%c("MATCH", "MISMATCH","secondaryMISMATCH_FSTzeroDiffFamily")),]
perpopREDEnclaves<-perpopRED[which(perpopRED$Mismatch3a%in%c("MATCH", "MISMATCH","secondaryMISMATCH_FSTzeroDiffFamily")),]
perpopRED$enclavesAgain<-NA
perpopRED$enclavesAgain[which(perpopRED$Mismatch3a=="MATCH")]<-"Match"
perpopRED$enclavesAgain[which(perpopRED$Mismatch3a=="MISMATCH")]<-"GeneticEnclave"
perpopRED$enclavesAgain[which(perpopRED$Mismatch3a=="secondaryMISMATCH_FSTzeroDiffFamily")]<-"LinguisticEnclave"
perpopREDnoDrift<-perpopRED[-which(perpopRED$listSingleCases=="Drifted"),]
library(ggrepel)
colorchoice2<-colorchoice
colorchoice2[10]<-"gray20"
colorchoice2[11:16]<-colorchoice[10:15]
ggplot(perpopREDnoDrift,aes(MedDiff,MedDiffCI, color=MainFamilies))+
geom_point()+
geom_text_repel(aes(label=PopName, color=MainFamilies), size=0.5)+
scale_color_manual(values = colorchoice2)+
theme_light()+ xlab("Difference Median FST between - within") +
ylab("Confidence Interval median difference")
ggsave("DifferenceMedianwithText.pdf", height = 12, width = 14, useDingbats=FALSE)
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 = colorchoice2)+
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)
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=5)
ggsave("DifferenceMedianLogScaleMajorLangFam.pdf", height = 12, width = 14, useDingbats=FALSE)
ggplot(perpopREDEnclaves,aes(MedDiff,MedDiffCI,color=Mismatch3a)) +
geom_point(alpha=0.5, size=3,show.legend = T) +
geom_text_repel(aes(label=PopName, color=Mismatch3a), size=0.8) +
geom_point(perpopREDnoDrift, aes(MedDiff,MedDiffCI),alpha=0.5, size=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()
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)
(fst1 | fst3) / fst2
ggsave("DifferenceMedianLogScaleGLOBALandMajorLangFam_3plots_2.pdf", height = 10, width = 12, useDingbats=FALSE)
###
# mark the mismatches in fst distribution
perpopRED$PopName[which(perpopRED$Mismatch3a=="MISMATCH")]
ListEnclaves<-perpopRED$PopName[which(perpopRED$ListEnclaves=="Enclave")]
Misalligned<-perpopRED$PopName[which(perpopRED$MedDiff<0&perpopRED$MedDiffCI<0.01)]
Misalligned<-c(Misalligned,"Hoan", "Naro")
Misalligned<-Misalligned[-which(Misalligned%in%ListEnclaves)]
## all the problematic cases together
library(reshape)
ListEnclaves<-perpopRED$PopName[which(perpopRED$ListEnclaves=="Enclave")]
intersecti<-intersect(DRIFTONI, ListEnclaves)
perpopRED$singlepops<-"ND"
perpopRED$singlepops[which(perpopRED$Mismatch3a =="MATCH")]<-"Match"
perpopRED$singlepops[which(perpopRED$PopName %in% DRIFTONI)]<-"Drifted"
perpopRED$singlepops[which(perpopRED$ListEnclaves=="Enclave")]<-"Enclave"
perpopRED$singlepops[which(perpopRED$PopName %in% Misalligned)]<-"Misalligned"
table(perpopRED$singlepops)
#**************************************************
### stacked bar proportion cases to exclude
# supplementary S10
#**************************************************
library(reshape)
perpopREDfamily<-perpopRED[which(perpopRED$glottolog.NAME %in% MainFamilies),]
size<-as.numeric(table(perpopREDfamily$MainFamilies))
proportionSingleCases<-table(perpopREDfamily$MainFamilies,perpopREDfamily$listSingleCases)/size
proportionSingleMELT<-melt(proportionSingleCases)
colnames(proportionSingleMELT)<-c("Family", "variable", "proportion")
gi<-ggplot(proportionSingleMELT, aes(Family, proportion))
ggplot(proportionSingleMELT, aes(fill=variable, y=proportion, x=Family)) +
geom_bar(position="fill", stat="identity", alpha=0.8)+
scale_fill_manual(values = c("purple", "pink", "#FC7A1E", "#8E3B46","darkblue", "69b3a2", "gray20"), name="")+
theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1)) +xlab("") +ylab("")
ggsave("proportionSingleCasesEachFamily_2.pdf", useDingbats=FALSE, width = 7, height = 7)
########
## the map with singular mismatches of linguistic and genetic migrants, misaligned and drifted
# Figure 1 A
########
library(ggrepel)
#enclavesByMistake<-c("Yoruba" ,"Nama", "Han-NChina" , "Bengali", "Evenk_FarEast") # these pops are not real enclaves, their linguistically unrelated pair is the one driving this genetic proximity effect
perpopRED2<-perpopRED[!is.na(perpopRED$lat),]
perpopREDSHIFTMAP<-MoveDataCoord(perpopRED2)
perpopREDSHIFTMAPinterest<-perpopREDSHIFTMAP[-which(perpopREDSHIFTMAP$listSingleCases=="ND"),]
#perpopREDSHIFTMAPmismatch<-perpopREDSHIFTMAPinterest[which(perpopREDSHIFTMAPinterest$singlepops%in%c("Misalligned","Enclave")),]
perpopREDSHIFTMAPnodata<-perpopREDSHIFTMAP[which(perpopREDSHIFTMAP$Mismatch3a=="ZeroSameFamilyNeighbors"),]
#perpopREDSHIFTMAPinterest<-perpopREDSHIFTMAPinterest[-which(perpopREDSHIFTMAPinterest$PopName%in% enclavesByMistake),]
#perpopREDSHIFTMAPmismatch<-perpopREDSHIFTMAPinterest[which(perpopREDSHIFTMAPinterest$Mismatch3a%in%c("MISMATCH","secondaryMISMATCH_FSTzeroDiffFamily")),]
### variant with language family color
# and symbols for single cases
base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color="black",shape=3,size=0.5)+
theme(legend.position="bottom")+
geom_point(data = perpopREDSHIFTMAP[which(perpopREDSHIFTMAP$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)
ggsave("WholeGelatoMap_Pacific_LittleCrossesMajorFamilies_SingleCases_rightSymbols.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
# ***************************************************
#### SUPPLEMENTARY FIGURES
# ***************************************************
# ***************************************************
# ***************************************************
# ***************************************************
## Figure S3
threshold<-c(500,1000,1500,2000,2500,3000)
blockneighborthresholdGeo<-c()
for (i in 1:length(threshold)){
testino2<-ddply(FstListREDinfo, "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$threshold<-threshold[i]
blockneighborthresholdGeo<-rbind(blockneighborthresholdGeo,testinomerged)
}
blockneighborthresholdGeoINFO<-merge(perpopRED,blockneighborthresholdGeo)
# violin plots with number of populations from a different language family
# fig S3B
blockneighborthresholdGeoINFO$threshold<-as.character(blockneighborthresholdGeoINFO$threshold)
blockneighborthresholdGeoINFO$threshold[which(blockneighborthresholdGeoINFO$threshold=="500")]<-" 500"
ga<-ggplot(blockneighborthresholdGeoINFO,aes(x=threshold,y=GeoCloseUnrelated1))
ga+ geom_jitter(shape=16, position=position_jitter(0.2))+
geom_violin(trim=FALSE, alpha=0.4)+
stat_summary(fun.data="mean_sdl", mult=1,
geom="pointrange", color="orange")+
theme_light()+
ylim(0,150)+xlab("Geographic radius in km")+ylab("number of populations from a different language family")
ggsave("Suppl_DensityDistributionMismatches_vsGeoDistanceThresholds_2021.pdf", useDingbats=FALSE, height = 4, width = 6, units = "in")
# frequencies at least one neighbor from diff family
freqOneneighbor<-c()
for (i in 1:length(threshold)){
thresholdtemp<-threshold[i]
popsNumberofNeighbors<-ddply(FstListREDinfo, "Pop1", function(x) length(which(x$GEOdist<thresholdtemp&x$FAMILY=="DIVERSE"))) # no continental filter
freqOneneighbor[i]<-length(which(popsNumberofNeighbors$V1>0))/404
}
round(freqOneneighbor*100)
[1] 57 85 93 98 99 100 # add manually on the violin plot figure
# ***************************************************
# 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(FstListREDinfo, "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
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(FstListREDinfo, "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$GeoCloseUnrelatedFamilies==0),]
perpopRED4_SHIFTMAP<-MoveDataCoord(perpopRED4)
# plot on a map the density of unrelated Language Families geographically close
# fig S3C
base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color = "black",
size = 0.5, shape=3)+
geom_point(data=perpopRED4_SHIFTMAP, aes(x = lon.1, y = lat.1,color=as.numeric(GeoCloseUnrelatedFamilies)), alpha=0.5, size=2)+
# ggtitle("number of neighbors from a different language family")+
scale_color_gradient(low = "blue", high = "red",name="") +
xlab("") + ylab("")
ggsave("DensityDifferentLFamilyNeighbors_noContinentFilter_map_PacificCenter_1000km_2021.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
#******************************************
# control the distribution of divergence time for each macro region
# fig S5
#******************************************
perpopREDNe<-perpopRED[which(perpopRED$USEforNe_calculation=="YES"),]
possiblepopswithNE<-perpopREDNe$PopName
meltFstREDinfoYESne<-FstListREDinfo[which(FstListREDinfo$Pop1%in%possiblepopswithNE&FstListREDinfo$Pop2%in%possiblepopswithNE),]
meltFstGlotto_infowithinREgionTMRCA<-meltFstREDinfoYESne[which(meltFstREDinfoYESne$region1==meltFstREDinfoYESne$region2),]
## exclude drifted pops or the Fst averages will be higher than normal
DRIFTONI<-perpopRED[which(perpopRED$medianFSTregion>0.1&perpopRED$medianFST>0.1),]$PopName
#DRIFTONI<-perpopRED[which(perpopRED$averageFSTregion>0.1&perpopRED$averageFST>0.1),]$PopName
meltFstGlotto_infowithinREgionTMRCA<-meltFstGlotto_infowithinREgionTMRCA[-c(which(meltFstGlotto_infowithinREgionTMRCA$Pop2%in%DRIFTONI), which(meltFstGlotto_infowithinREgionTMRCA$Pop1%in%DRIFTONI)),]
ggplot(meltFstGlotto_infowithinREgionTMRCA, aes(region1, TMRCA_doubleNe))+
geom_jitter(shape=16, position=position_jitter(0.2))+
geom_violin(trim=FALSE, alpha=0.4)+
stat_summary(fun.data="mean_sdl", mult=1,
geom="pointrange", color="orange")+
ylim(0,70000)+xlab("")+ylab("population divergence time, years ago")
ggsave("distribTMRCA_ContinentsViolin.pdf", useDingbats=FALSE, width=6, height = 4)
#******************************************
#******************************************
#******************************************
#******************************************
# ANALYSIS PERCENTILES
#******************************************
# OVERVIEW OF MISMATCHES WITH CLOSE FST DISTANCES
#******************************************
#******************************************
#* Figure S6
# ------------------------------------------------------
# CONTINENT MEDIAN FST and a range of percentile threshold values
## adjust dataset without very Drifted and within and between regions
FstListGlotto_infowithinREgion<-FstListREDinfo[which(FstListREDinfo$region1==FstListREDinfo$region2),]
## exclude drifted pops or the Fst averages will be higher than normal
DRIFTONI<-perpopRED[which(perpopRED$medianFSTregion>0.1&perpopRED$medianFST>0.1),]$PopName
FstListGlotto_infowithinREgionNoDrif<-FstListGlotto_infowithinREgion[-c(which(FstListGlotto_infowithinREgion$Pop%in%DRIFTONI), which(FstListGlotto_infowithinREgion$Pop1%in%DRIFTONI)),]
FstListGlottoIBD_infoNoDrift<-FstListREDinfo[-c(which(FstListREDinfo$Pop2%in%DRIFTONI), which(FstListREDinfo$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$Fst),aggiuntaWorld)
medianGEO<-rbind(medianGEO,aggiuntaWorldline)
# assign the lowest percentile to each pair fst
FstListREDinfobeforetest<-FstListREDinfo
FstListREDinfobeforetesttoday<-FstListREDinfo
FstListREDinfo$percentileFST<-NA
for (i in 1:nrow(FstListREDinfo)){
if(FstListREDinfo$region1[i]!=FstListREDinfo$region2[i] ){
FstListREDinfo$percentileFST[i]<- percentiles[which(FstListREDinfo$Fst[i]<aggiuntaWorld)][1]
}
else {
reftemp<- valuespercentileRegions[which( medianGEO$region1== FstListREDinfo$region2[i]),] # list of percentiles corresponding to the continent of the two populations
FstListREDinfo$percentileFST[i]<- percentiles[which(FstListREDinfo$Fst[i]<reftemp)][1]
}
}
# prepare plottable file for pairwise connections
betweenfamilSMALLgeoplottabile1<-FstListREDinfo
betweenfamilSMALLgeoplottabile1$index<-c(1:nrow(FstListREDinfo))
betweenfamilSMALLgeoplottabile1<-betweenfamilSMALLgeoplottabile1[,-which(colnames(betweenfamilSMALLgeoplottabile1)%in%c("lat2","lon2"))]
betweenfamilSMALLgeoplottabile2<-FstListREDinfo
betweenfamilSMALLgeoplottabile2$index<-c(1:nrow(FstListREDinfo))
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$FAMILY=="DIVERSE"),]
fig6a<-base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color = "black",
size = 0.5, shape=3)+
geom_path(data=betweenfamilSMALLgeoplottabileSHIFTMAP3, aes(x = lon.1, y = lat.1,
group=index, alpha=percentileFST, size=percentileFST, color=percentileFST))+
scale_alpha(range = c(0.2,0.1))+
scale_size(range = c(0.5,.1))+
scale_colour_gradient2(midpoint=0.1, low="darkred", mid="darkorange",
high="white", name="Percentile FST distribution")+
#ggtitle("weight of mismatches according to FST percentile distribution")+
xlab("") + ylab("") +
guides(alpha=FALSE, size=FALSE)
### Supplementary Figure S6A
ggsave("MapPairMismatchesPercentileDistribution_until10percentile_2021.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
# ***************************************************
# sensitivity test with different geo thresholds
# Figure S2
### all the close FST distances on a map
base_plot + geom_point(data = perpopREDSHIFTMAP,
aes(x = lon.1, y = lat.1),
color = "black",
size = 0.5, shape=3)+
geom_path(data=betweenfamilSMALLgeoplottabileSHIFTMAP2, aes(x = lon.1, y = lat.1,
group=index, alpha=percentileFST, color=percentileFST))+
scale_alpha(range = c(0.2,0.01))+
scale_size(range = c(0.5,.01))+
scale_colour_gradient2(midpoint=0.05, low="purple", mid="yellow",
high="white", name="Percentile FST distribution")+
xlab("") + ylab("") +
guides(alpha=FALSE, size=FALSE)
ggsave("MapPairFSTPercentileDistribution_2021.pdf", useDingbats=FALSE, width = 15, height = 8, dpi = 300, units = "in")
##-------------------------------------
# figure area proportion of linguistically unrelated in close FSTs
# supplementary Fig S6B
##-------------------------------------
#FstListREDinfo_diverse<-FstListREDinfo[which(FstListREDinfo$FAMILY=="DIVERSE"),]
FstListGlotto_infowithinREgion<-FstListREDinfo[which(FstListREDinfo$region1==FstListREDinfo$region2),]
FstListREDinfo_BetweenRegion<-FstListREDinfo[which(FstListREDinfo$region1!=FstListREDinfo$region2),]
numberTotalSmallFST<-c()
numberTotalSmallFSTDIVERSE<-c()
percentageMismatchesinTotalSmallFST<-c()
#write.table(medianGEO, "medianGEO.txt", sep = " ")
meltFstGlottoIBD_infoWithPercentageMismatch<-data.frame(row.names = FstListREDinfo$popslistemp)
for (j in 1:length(percentiles)){
mismatchlist<-c()
percTarget<-percentiles[j]
for (i in 1:5){ # the five macro continents
mismatchlist1<-FstListGlotto_infowithinREgion[which(FstListGlotto_infowithinREgion$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$popslistemp,j]<-percTarget
}
colnames(meltFstGlottoIBD_infoWithPercentageMismatch)<-percentiles
#meltFstGlottoIBD_infoWithPercentageMismatchSort<-
# meltFstGlottoIBD_infoWithPercentageMismatch[FstListREDinfo$popslistemp,]
listperpercentileNumberCases<-rbind(numberTotalSmallFST,numberTotalSmallFSTDIVERSE,percentageMismatchesinTotalSmallFST)
colnames(listperpercentileNumberCases)<-percentiles
# count the cases below each percentile
f<-function(x, output){sort(x)[1]}
FstListREDinfo$percentileFSTdoublecheck<-apply(meltFstGlottoIBD_infoWithPercentageMismatch,
1, f)
plot(FstListREDinfo$percentileFSTdoublecheck, FstListREDinfo$percentileFST) # check if the two slightly different method give the same percentiles
#plotproportion<-data.frame(percentiles,percentageMismatchesinTotalSmallFST)
plotproportion<-data.frame(percentiles,nrow(FstListREDinfo)-numberTotalSmallFST,numberTotalSmallFST-numberTotalSmallFSTDIVERSE,numberTotalSmallFSTDIVERSE)
colnames(plotproportion)<-c("percentiles","pairsOutside","pairsInsideSameFamily","pairsInsideDiffFamily")
fig6B<-ggplot(plotproportion,aes(x=as.numeric(percentiles), y=percentageMismatchesinTotalSmallFST, color=as.numeric(percentiles)))+
geom_segment(aes(xend=as.numeric(percentiles), yend=0, color=as.numeric(percentiles)), alpha=0.9)+
geom_line(size=1)+
# geom_area(aes(x=as.numeric(percentiles), y=percentageMismatchesinTotalSmallFST), alpha=0.5, fill="darkorange", color="darkorange")+
#ggtitle("proportion of couples from Diff L Families over number of pairs genetically close - FST percentile distrib")+
labs(x="Percentile global Fst distribution", y="proportion of pairs from Different Language Families")+
#geom_vline(xintercept = 0.2, color="darkorange", size=1)+
scale_colour_gradient2(midpoint=0.2, low="darkred", mid="darkorange",
high="white", name="")+
theme_light() +
theme(legend.position = "none", text=element_text(size=5))
ggsave("ProportionaMismatchesSensitivityThresholdFST_2021_better.pdf", useDingbats=FALSE, width = 4, height = 2)
### for each population, i annotate the percentile FST threshold in which they appear in a mismatch pair
perpopRED$percentileMismatch<-NA
singlepopinmismatch<-c()
FstListREDinfoDIVERSE<-FstListREDinfo[which(FstListREDinfo$FAMILY=="DIVERSE"),]
for (i in length(percentiles):1){
percTarget<-percentiles[i]
temp<-FstListREDinfoDIVERSE[which(FstListREDinfoDIVERSE$percentileFST<=percTarget),]
listoni<- unique(temp$Pop1)
perpopRED$percentileMismatch[which(perpopRED$PopName%in%listoni)]<-percTarget
singlepopinmismatch[i]<-length(listoni)
}
# CONNECT MISMATCHES BETWEEN MAJOR FAMILIES
# with a big circle
##### FIGURE 1C ****************************************
perpopREDfamily<-perpopRED[which(perpopRED$glottolog.NAME %in% MainFamilies),]
meltFstGlottoIBD_infoLessPercentile<-FstListREDinfo[which(FstListREDinfo$percentileFST<0.1),] # the closest 0.1 percentile
meltFstREDinfoMAJORFAM<-meltFstGlottoIBD_infoLessPercentile[which(meltFstGlottoIBD_infoLessPercentile$FAMILY=="DIVERSE"),]
meltFstREDinfoMAJORFAM<-meltFstREDinfoMAJORFAM[which(meltFstREDinfoMAJORFAM$family1%in%MainFamilies&meltFstREDinfoMAJORFAM$family2%in%MainFamilies),]
tablonMismatchMajorFamilies<-table(meltFstREDinfoMAJORFAM$family1,meltFstREDinfoMAJORFAM$family2)
mat<-as.matrix(tablonMismatchMajorFamilies)
orderfamily<-c("Kxa","Khoe-Kwadi" ,"Atlantic-Congo","Afro-Asiatic","Turkic" ,"Indo-European" ,"Uralic",
"Abkhaz-Adyge" ,"Nakh-Daghestanian", "Tungusic", "Mongolic-Khitan","Sino-Tibetan",
"Austronesian" ,"Quechuan","Tupian")
orderspecial<-orderfamily[which(orderfamily%in%rownames(mat))]
matorder<-mat[orderspecial,]
matorder2<-matorder[,orderspecial]
rownames(MainFamilies2)<-MainFamilies2$MainFamilies
MainFamiliesRED<-MainFamilies2[orderspecial,]
grid.col<-(MainFamiliesRED$COLOR)
names(grid.col)<-MainFamiliesRED$MainFamilies # create a color combination with the color palette
library(circlize)
# adjust for sample size TOTAL DATASET between major families
meltFstREDinfo222<-FstListREDinfo[which(FstListREDinfo$family1%in%MainFamilies),]
meltFstREDinfo222<-meltFstREDinfo222[which(meltFstREDinfo222$family2%in%MainFamilies),]
#samplesize1<-table(c(meltFstREDinfo222$family1,meltFstREDinfo222$family2)) # all the possible pairs for each language family but only between other main language families
samplesize1<-table(meltFstREDinfo222$family1)
orderspecialsamplesize<-samplesize1[orderspecial]
matAdjustSize<-matorder2
for(j in 1:nrow(matorder2)){
for (k in 1:ncol(matorder2)){
matAdjustSize[j,k]<-((matorder2[j,k]/(orderspecialsamplesize[j]*orderspecialsamplesize[k])))
}
}
matAdjustSizeROUND<-round(matAdjustSize/min(matAdjustSize[-which(matAdjustSize==0)]))
matAdjustSizeROUND<-as.matrix(matAdjustSizeROUND)
# set color proportional to average lower percentile FST
matFSTpercentile<-matAdjustSizeROUND
for(j in 1:nrow(matAdjustSizeROUND)){
for (k in 1:ncol(matAdjustSizeROUND)){
coppia<-c(rownames(matAdjustSizeROUND)[j],colnames(matAdjustSizeROUND)[k])
templist<-meltFstREDinfoMAJORFAM[which(meltFstREDinfoMAJORFAM$family1%in%coppia),]
# templist2<-meltFstREDinfoMAJORFAM[which(meltFstREDinfoMAJORFAM$family2==rownames(matorder2)[j]&meltFstREDinfoMAJORFAM$family1==colnames(matorder2)[k]),]
# templist<-rbind(templist, templist2)
matFSTpercentile[j,k]<-median(templist$percentileFST)
}
}
matFSTpercentilemelt<-melt(matFSTpercentile)
listvalues<-sort(unique(matFSTpercentile))
sort(unique(matFSTpercentile))
[1] 0.046 0.048 0.049 0.050 0.052 0.056 0.057 0.058 0.059 0.060 0.062 0.063 0.064 0.065 0.066 0.068 0.070 0.071 0.074 0.076 0.078
[22] 0.086
colfunc <- colorRampPalette(c("darkred", "darkorange"))
listcolors<-colfunc(length((listvalues)))
listvalues<-cbind(listvalues,listcolors)
matFSTpercentilemeltcolor<- listvalues[,2][match(matFSTpercentilemelt[,3],listvalues[,1])]
matpercentilecolor<-matrix(matFSTpercentilemeltcolor,
nrow(matAdjustSizeROUND),
ncol(matAdjustSizeROUND))
pdf("circlize_Mismatches_families_adjustSampleSize_below10percentile.pdf", width = 8, height = 8)
ff<-chordDiagramFromMatrix(matAdjustSizeROUND, directional = 0,
transparency = 0.1, symmetric=T, order = orderspecial,
col=matpercentilecolor,grid.col = grid.col)
dev.off()
#******************************************
## figure distribution FST SAME OR DIFFERENT FAMILY for each population
## Figure 1D, case studies
#******************************************
# subset case studies
casestudies<-c( "Hungarian1", "Maltese", "Azeri_Azerbajan", "Kalmyk","Tunisian","Armenian" )
#casestudies<-c( "Wayku", "Azeri_Azerbajan", "Kalmyk","Cusco2","Tunisian","Armenian" )
#casestudies<-c( "Wayku","Cusco2")
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.data="mean_sdl",
geom="pointrange", color="gold") +
theme_light()+
scale_y_log10()+
scale_color_manual(values=c("#198B9F","#BA1E1E"))+
facet_wrap(~TARGET,nrow = 2)
ggsave("caseStudyMisalignedVIOLIN_YESorNO.pdf", height=5,width=5, useDingbats=FALSE)
ggsave("caseStudyMisalignedVIOLIN_YESorNO2.pdf", height=5,width=5, useDingbats=FALSE)
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# ANALYSIS OF REGIONAL CASE STUDIES
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# EUROPE