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Notes5_Mar2023_Rplots_Admix5groups_PCA_PopStats.R
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#####################################
# Entire Cassiope analysis - part 5
# March 2023
# R plotting
############################
# File contains:
# - List of files downloaded from Cedar
# - Package installations
# - libraries
# - Load in all the data
# PCA analysis
# Join all the data into population file and sample file
# Admixture
# - Determine Admixture groups order
# - Set groups colours
# - Plot PCA images with colours
# - Plot map of sites with dominant colours
# - Make Admixture barplot (total, Ancient, Ellemere)
# - Make Admixture maps (samples and population scales)
# - Kluane correlation and barplot
# PopStats
# - Icetime, Diversity and Temperature correlations
# - Plots of Pi and Tajima's D per population
# - Maps of all population avgeraged stats
# Table of PopStats for paper
# Site specific Admixture plots
#####################################
# download files from Cedar to computer
# Admixture
# cd /~/projects/rpp-rieseber/celphin/Cassiope/Admixture_March2023/Take1
# .4.Q
# .5.Q
# .10.Q
# # PopStats
# cd /~/scratch/celphin/Cassiope/PopStats_Splitstree_March2023/
# Fst_mean.txt
# Fst_weighted.txt
# Fst_count.txt
# Fst_sites.txt
# population_TajimaD.txt
# summed_site_Pi.txt
# summed_wind_Pi.txt
# Het_data_by_pop.txt
# # PCA GDS file
# cd /~/scratch/celphin/Cassiope/PopStats_Splitstree_March2023/PCA_input
# Cassiope_noMER_r10i.recode.gds
# # SplitsTree
# cd /~/scratch/celphin/Cassiope/PopStats_Splitstree_March2023/SplitsTree/PGDSpider_2.1.1.5/data/
# output_Nexus3.nexus
######################################
# # install packages
# install.packages("devtools")
# devtools::install_github("MikkoVihtakari/PlotSvalbard", upgrade = "never")
# devtools::install_github("celphin/Equitable")
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("SNPRelate")
#
# install.packages(c("tidyverse","dplyr","vctrs", "tidyr","reshape","geosphere","ade4","ape","scatterpie","data.table","maps","readr","stringr","ggplot2"))
#----------------------------------
# libraries
library(Equitable)
#library(PlotSvalbard)
library(tidyverse)
library(dplyr)
library(tidyr)
library(reshape)
library(geosphere)
library(ade4)
library(ape)
library(scatterpie)
library(data.table)
library(maps)
library(readr)
library(stringr)
library(SNPRelate)
library(ggplot2)
##############################
# Load in the data
setwd("~/GitHub/Population_genomics_Cassiope")
#Load the gds file - for PCA
genofile <- snpgdsOpen("./Figures_data/Cassiope_noMER_r10i.recode.gds")
# load list of samples in filtered vcf
samples_list <- read.table("./Figures_data/Cassiope_noMER_r10i.imiss", header = TRUE)
# load in Admixture data - code setup for 5 populations
Admix_tbl=read.table("./Figures_data/Admixture/Take1/Cassiope_noMER_r10i.5.Q")
# load detailed information about all 371 individuals
sample_information <- read.csv("./Figures_data/All_samples_lat_long_leaf_icetimes.csv", header = TRUE)
# load in population specific information about sites
population_information <- read.csv("./Figures_data/Pop_LatLong_Icetimes_Climate.csv", header = TRUE)
# PopStats
Het_data <- read.table("./Figures_data/PopStats/Het_data_by_pop.txt", header = TRUE)
Pi_wind_data <- read.table("./Figures_data/PopStats/summed_wind_Pi.txt", header=TRUE, sep=" ")
Pi_site_data <- read.table("./Figures_data/PopStats/summed_site_Pi.txt", header=TRUE, sep=" ")
Tajima_data <- read.table("./Figures_data/PopStats/population_TajimaD.txt", header=TRUE, sep=" ")
#################################
# PCA
# https://owensgl.github.io/biol525D/Topic_8-9/pca.html
# create GDS file on server
# snpgdsVCF2GDS("/scratch/celphin/GBS_Cassiope/Mar2020_SNPFiltering_PopStats/R_plots/FiltXXg9mac5minq30r60i_chrom_rLD.vcf.gz",
# "/scratch/celphin/GBS_Cassiope/Mar2020_SNPFiltering_PopStats/R_plots/FiltXXg9mac5minq30r60i_chrom_rLD.gds",
# method="biallelic.only")
#Prune for linkage
snpset_pruned <- snpgdsLDpruning(genofile, autosome.only=F)
#Make a list of sites we're keeping.
snpset.id <- unlist(snpset_pruned)
#Run the PCA
pca0 <- snpgdsPCA(genofile, num.thread = 1, eigen.cnt = 16, snp.id = snpset.id, missing.rate = 0.1, autosome.only = F)
pca5 <- snpgdsPCA(genofile, num.thread = 1, eigen.cnt = 16, snp.id = snpset.id, missing.rate = 0.1, maf=0.05, autosome.only = F)
###################################
pca <- pca5
#Here's the percent variance explained for each eigenvector
pc.percent <- pca$varprop*100
round(pc.percent, 2)
# MAF 1%
# 27.00 5.62 2.63 1.63 1.50 1.26 1.02 0.93 0.93 0.85 0.82 0.77 0.73 0.73 0.68 0.67
# MAF 5%
# 10.08 5.25 3.57 3.28 1.86 1.57 1.37 1.26 1.20 1.19 1.10 0.92 0.82 0.79 0.78 0.76
#Make a dataframe of your PCA results
PCA_tab <- data.frame(sample = pca$sample.id,
PC1 = pca$eigenvect[,1], # the first eigenvector
PC2 = pca$eigenvect[,2], # the second eigenvector
PC3 = pca$eigenvect[,3], # the first eigenvector
PC4 = pca$eigenvect[,4], # the second eigenvector
PC5 = pca$eigenvect[,5], # the first eigenvector
PC6 = pca$eigenvect[,6], # the second eigenvector
stringsAsFactors = FALSE)
str_split(as.character(PCA_tab$sample), "_")
q <- as.data.frame(t(as.matrix(as.data.frame((strsplit(as.character(PCA_tab$sample), "_"))))))
PCA_tab_data <- cbind(PCA_tab, q[,1])
colnames(PCA_tab_data) <- c("ID_code", "PC1", "PC2", "PC3", "PC4", "PC5", "PC6","Pop")
#####################################
# Join all the data
# Sample specific information
# samples_list
# Admix_tbl
# sample_information
# Het_data
# PCA_tab_data
# Join Samples information
samples_list$ID_code <- as.factor(samples_list$INDV)
ID_code = samples_list$ID_code
sample_information$Pop[which(sample_information$Pop=="25")] <- "025"
Admix_tbl1 <- cbind(ID_code, Admix_tbl)
All_samples_data <- left_join(Admix_tbl1, sample_information, by="ID_code")
All_samples_data <- left_join(All_samples_data, Het_data, by="ID_code")
All_samples_data <- left_join(All_samples_data, samples_list, by="ID_code")
All_samples_data <- left_join(All_samples_data, PCA_tab_data, by="ID_code")
#------------------------------
# join all Population information
# population_information
# Pi_wind_data
# Pi_site_data
# Tajima_data
colnames(Pi_site_data) <- c("Pop" , "SumSitePi" , "Site_SD_Pi", "Site_var_Pi")
colnames(Pi_wind_data) <- c("Pop" , "SumWindPi", "Wind_SD_Pi", "Wind_var_Pi")
colnames(Tajima_data ) <- c("Pop" , "TajimaAvg", "TajimaSD", "TajimaVar")
population_information$Pop[which(population_information$Pop=="25")] <- "025"
All_pop_data <- left_join(population_information, Tajima_data, by="Pop")
All_pop_data <- left_join(All_pop_data, Pi_site_data, by="Pop")
All_pop_data <- left_join(All_pop_data, Pi_wind_data, by="Pop")
#------------------------------
# average the sample information by population
Pop_avg <- All_samples_data %>%
group_by(Pop.x) %>%
dplyr::summarise(V1 = mean(V1, na.rm=TRUE),
V2 = mean(V2, na.rm=TRUE),
V3 = mean(V3, na.rm=TRUE),
V4 = mean(V4, na.rm=TRUE),
V5 = mean(V5, na.rm=TRUE),
# V6 = mean(V6, na.rm=TRUE),
# V7 = mean(V7, na.rm=TRUE),
# V8 = mean(V8, na.rm=TRUE),
# V9 = mean(V9, na.rm=TRUE),
# V10 = mean(V10, na.rm=TRUE),
Lat = mean(Lat, na.rm=TRUE),
Long = mean(Long, na.rm=TRUE),
Lat_shift = mean(Lat_shift, na.rm=TRUE),
Long_shift = mean(Long_shift, na.rm=TRUE),
Avg.Temp = mean(Avg.Temp, na.rm=TRUE),
Leaf_weight = mean(Leaf_weight, na.rm=TRUE),
Ice_time = mean(Ice_time, na.rm=TRUE),
O.HOM. = mean(O.HOM., na.rm=TRUE),
E.HOM. = mean(E.HOM., na.rm=TRUE),
N_SITES = mean(N_SITES, na.rm=TRUE),
FIS = mean(F, na.rm=TRUE),
N_MISS = mean(N_MISS, na.rm=TRUE),
F_MISS = mean(F_MISS, na.rm=TRUE),
PC1 = mean(PC1, na.rm=TRUE),
PC2 = mean(PC2, na.rm=TRUE),
PC3 = mean(PC3, na.rm=TRUE),
PC4 = mean(PC4, na.rm=TRUE),
PC5 = mean(PC5, na.rm=TRUE),
PC6 = mean(PC6, na.rm=TRUE)
)
Pop_avg$Pop <- Pop_avg$Pop.x
All_pop_data <- left_join(All_pop_data, Pop_avg, by="Pop")
############################################
# calculate the max Admix group for each location
Admix_groups <- gather(All_pop_data, Group, Amount, V1:V5, factor_key=TRUE)
maxAdmixgroup <- Admix_groups %>%
group_by(Pop) %>%
dplyr::summarise(Amount = max(Amount, na.rm=TRUE))
Final_AdmixGroups <- left_join(maxAdmixgroup, Admix_groups, by="Amount")
All_pop_data$Max_Admix_Group <- Final_AdmixGroups$Group
Admix_K_Region <- All_pop_data %>%
group_by(Group) %>%
dplyr::summarise(Max_Admix_Group = list(Max_Admix_Group))
# 5 groups
# V5 - Alaska/NWT
# V2 - Europe
# V4 - Greenland
# V1 - Russia
# V3 - Saximontana
Max_Admix_Group <- c("V1", "V2", "V3", "V4", "V5")
Admix_Location <- c("Russia", "Europe", "Saximontana", "Greenland", "Alaska")
Group_location <- as.data.frame(cbind(Max_Admix_Group, Admix_Location))
All_pop_data <- left_join(All_pop_data, Group_location, by="Max_Admix_Group")
#5 groups
Groups_summary_admix <- All_pop_data %>%
group_by(Admix_Location) %>%
dplyr::summarise(Locations = list(Pop),
Lat = mean(Lat.x, na.rm=TRUE),
Long = mean(Long.x, na.rm=TRUE),
V1 = mean(V1, na.rm=TRUE),
V2 = mean(V2, na.rm=TRUE),
V3 = mean(V3, na.rm=TRUE),
V4 = mean(V4, na.rm=TRUE),
V5 = mean(V5, na.rm=TRUE))
###################################
# Set up colour scheme
map_colours_5g <- c("red", "yellow", "purple", "orange", "deepskyblue")
# Alaska - "deepskyblue"
# Greenland - "lightorange"
# Saximontana - "purple"
# Europe - "yellow"
# Russia - "red"
map_col <- cbind(Max_Admix_Group, map_colours_5g)
map_col <- as.data.frame(map_col)
All_pop_data <- left_join(All_pop_data, map_col, by="Max_Admix_Group")
# join map_colours with individual sample data
Map_col_pop <- as.data.frame(cbind(All_pop_data$Pop, All_pop_data$map_colours_5g, All_pop_data$Max_Admix_Group))
colnames(Map_col_pop) <- c("Pop", "map_colours_5g", "Max_Admix_Group")
All_samples_data$Pop <- All_samples_data$Pop.x
All_samples_data <- left_join(All_samples_data, Map_col_pop, by="Pop")
#####################################
#Plot a PCA image
# MAF 1%
# PCA = 27.00 5.62 2.63 1.63 1.50 1.26 1.02 0.93 0.93 0.85 0.82 0.77 0.73 0.73 0.68 0.67
colnames(All_samples_data)[2:6] <- c("Russia", "Europe", "Subspp", "Greenland", "Alaska")
png("./Figures_data/Plots/PCA_PC1_PC2_maf1.png", width = 3000, height = 2700)
All_samples_data %>%
ggplot(.,aes(x=PC1,y=PC2)) +
geom_point(aes(color = Max_Admix_Group), size=15) +
theme_classic()+
theme(legend.text = element_text(color = "black", size = 70),
axis.text=element_text(size=60 ,face="bold"),
axis.title=element_text(size=60,face="bold")) +
guides(colour = guide_legend(override.aes = list(size=60)))+
scale_colour_manual(values = map_colours_5g)+
labs(y= "PC2 (5.62%)", x = "PC1 (27.00%)")
dev.off()
png("./Figures_data/Plots/PCA_PC2_PC3_maf1.png", width = 3000, height = 2700)
All_samples_data %>%
ggplot(.,aes(x=PC2,y=PC3)) +
geom_point(aes(color = Max_Admix_Group), size=15) +
theme_classic()+
theme(legend.text = element_text(color = "black", size = 70),
axis.text=element_text(size=60 ,face="bold"),
axis.title=element_text(size=60,face="bold")) +
guides(colour = guide_legend(override.aes = list(size=60)))+
scale_colour_manual(values = map_colours_5g)+
labs(y= "PC3 (2.63%)", x = "PC2 (5.62%)")
dev.off()
png("./Figures_data/Plots/PCA_PC3_PC4_maf1.png", width = 3000, height = 2700)
All_samples_data %>%
ggplot(.,aes(x=PC3,y=PC4)) +
geom_point(aes(color = Max_Admix_Group), size=15) +
theme_classic()+
theme(legend.text = element_text(color = "black", size = 70),
axis.text=element_text(size=60 ,face="bold"),
axis.title=element_text(size=60,face="bold")) +
guides(colour = guide_legend(override.aes = list(size=60)))+
scale_colour_manual(values = map_colours_5g)+
labs(y= "PC4 (1.63%)", x = "PC3 (2.63%)")
dev.off()
png("./Figures_data/Plots/PCA_PC5_PC6_maf1.png", width = 3000, height = 2700)
All_samples_data %>%
ggplot(.,aes(x=PC5,y=PC6)) +
geom_point(aes(color = Max_Admix_Group), size=15) +
theme_classic()+
theme(legend.text = element_text(color = "black", size = 70),
axis.text=element_text(size=60 ,face="bold"),
axis.title=element_text(size=60,face="bold")) +
guides(colour = guide_legend(override.aes = list(size=60)))+
scale_colour_manual(values = map_colours_5g)+
labs(y= "PC6 (1.26%)", x = "PC5 (1.50%)")
dev.off()
colnames(All_samples_data)[2:6] <- c("V1", "V2", "V3", "V4", "V5")
#########################################
# Map of samples
# https://mikkovihtakari.github.io/PlotSvalbard/articles/PlotSvalbard_user_manual.html
All_pop_data <- transform_coord(All_pop_data, lon = "Long.x", lat = "Lat.x", bind = TRUE, proj.og = "+proj=longlat +datum=WGS84", proj.out = "+init=epsg:3995")
All_pop_data$Pop <- as.factor(All_pop_data$Pop)
png("./Figures_data/Plots/Cassiope_samples_map_colours.png", width = 2000, height = 1400)
basemap("panarctic", limits=50) +
geom_point(aes(x = lon.utm, y = lat.utm), data = All_pop_data, col=All_pop_data$map_colours_5g, size=15)
dev.off()
############################################
#Make Admixture barplot
#Excel: =RIGHT(A2,LEN(A2) - SEARCH("_", A2, SEARCH("_", A2) + 1))
mergedAdmixTable <- All_samples_data[,c("Pop.x", "ID_code","V1", "V2","V3", "V4" ,"V5", "Lat", "Long")]
ordered1 = mergedAdmixTable[order(mergedAdmixTable$Long),]
rownames(mergedAdmixTable) <- mergedAdmixTable$ID_code
row_order <- c( "PopGEN_10", "PopGEN_1", "PopGEN_2", "PopGEN_3", "PopGEN_5", "PopGEN_6",
"PopGEN_7", "PopGEN_8", "PopGEN_9",
"PopKL1_7", "PopKL1_9", "PopKL4_1", "PopKL4_4", "PopKL3_1", "PopKL3_7", "PopKL5_1", "PopKL5_7", "PopKL2_1",
"PopKL2_2", "PopPC5_1", "PopPC5_2", "PopPC4_1", "PopPC4_2", "PopPC3_1", "PopPC3_2", "PopPC2_1",
"PopPC2_2", "PopPC1_2",
"PopATQ_17", "PopATQ_18", "PopATQ_19", "PopATQ_21", "PopATQ_22", "PopATQ_23", "PopATQ_25", "PopATQ_30",
"PopATQ_36", "PopATQ_37", "PopBARD_10", "PopBARD_11", "PopBARD_14", "PopBARD_15", "PopBARD_16","PopBARD_1",
"PopBARD_2", "PopBARD_37", "PopBARD_38", "PopBARD_9", "PopMAT_11", "PopMAT_13", "PopMAT_18", "PopMAT_1",
"PopMAT_2", "PopMAT_3", "PopMAT_5", "PopMAT_7", "PopMAT_8", "PopMNT_12", "PopMNT_17", "PopMNT_18",
"PopMNT_21", "PopMNT_22", "PopMNT_23", "PopMNT_4", "PopMNT_5", "PopMNT_7", "PopMNT_9", "PopIMN_11",
"PopIMN_13", "PopIMN_15", "PopIMN_17", "PopIMN_1", "PopIMN_20", "PopIMN_21", "PopIMN_2", "PopIMN_5",
"PopIMN_7", "PopSAG_13", "PopSAG_14", "PopSAG_16", "PopSAG_21", "PopSAG_23", "PopSAG_24", "PopSAG_32",
"PopSAG_38", "PopSAG_5", "PopSAG_9", "PopDEN_15", "PopDEN_1", "PopDEN_2", "PopDEN_2u", "PopDEN_3",
"PopDEN_3u", "PopDEN_4", "PopDEN_4u", "PopDEN_55", "PopDEN_5", "PopMIL_11", "PopMIL_14", "PopMIL_17",
"PopMIL_1", "PopMIL_20", "PopMIL_25", "PopMIL_2", "PopMIL_37", "PopMIL_5", "PopMIL_6", "PopQHI_38",
"PopQHI_37", "PopQHI_34", "PopQHI_33", "PopQHI_32", "PopQHI_31", "PopQHI_24", "PopQHI_22",
"PopPEA_11", "PopPEA_18", "PopPEA_25", "PopPEA_26", "PopPEA_27",
"PopPEA_28", "PopPEA_30", "PopPEA_34", "PopPEA_36", "PopKUQ_14", "PopKUQ_20", "PopKUQ_26", "PopKUQ_37",
"PopKUQ_4", "PopKUQ_9", "PopYAM_10", "PopYAM_1", "PopYAM_3", "PopYAM_4", "PopYAM_5", "PopYAM_6",
"PopYAM_7", "PopYAM_8", "PopYAM_9", "PopAXE_11", "PopAXE_13", "PopAXE_14", "PopAXE_21", "PopAXE_26",
"PopAXE_27", "PopAXE_2", "PopAXE_34", "PopAXE_6",
"PopCR_10", "PopCR_12", "PopCR_13", "PopCR_17", "PopCR_1", "PopCR_2", "PopCR_4", "PopCR_5",
"PopCR_7", "PopCR_9", "PopIq_10", "PopIq_13", "PopIq_15", "PopIq_16", "PopIq_17", "PopIq_20",
"PopIq_2", "PopIq_3", "PopIq_9","PopKik_11", "PopKik_13", "PopKik_16", "PopKik_17", "PopKik_18", "PopKik_1",
"PopKik_20", "PopKik_2", "PopKik_3",
"PopDLG_10", "PopDLG_11", "PopDLG_12", "PopDLG_13", "PopDLG_14",
"PopDLG_16", "PopDLG_17", "PopDLG_7", "PopDLG_8", "PopDLG_9", "PopDQG_10", "PopDQG_11", "PopDQG_12",
"PopDQG_13", "PopDQG_14", "PopDQG_1", "PopDQG_2", "PopDQG_3", "PopDQG_4", "PopDQG_6", "Pop025_10",
"Pop025_11", "Pop025_12", "Pop025_15", "Pop025_20", "Pop025_24", "Pop025_5", "Pop025_6", "Pop025_7",
"Pop025_9", "PopIG_10", "PopIG_12", "PopIG_14", "PopIG_2", "PopIG_3", "PopIG_4", "PopIG_5",
"PopIG_6", "PopIG_9", "PopEUR_1", "PopEUR_22", "PopEUR_34", "PopEUR_5",
"PopFOS_2", "PopFOS_3", "PopFOS_7", "PopFOS_8", "PopHAZ_18", "PopHAZ_20", "PopHAZ_22",
"PopHAZ_24", "PopHAZ_26", "PopHAZ_29", "PopHAZ_33", "PopHAZ_34", "PopHAZ_6", "PopHAZ_9",
"PopGF_1", "PopGF_4", "PopSVO_38", "PopBY1_14",
"PopBY1_1", "PopBY1_5", "PopBY1_7", "PopBY2_12", "PopBY2_3", "PopBY2_9", "PopBY3_14", "PopBY3_18",
"PopBY3_19", "PopAlexNew_49", "PopAlexNew_51", "PopAlexNew_46", "PopAlexNew_47", "PopAlexNew_36",
"PopAlexNew_28", "PopAlexNew_24", "PopAlexNew_6", "PopAlexNew_21", "PopAlexNew_68", "PopAlexNew_73",
"PopAlexNew_33", "PopAlexNew_32", "PopAlexNew_42","PopAlexOld_37", "PopAlexOld_24","PopAlexOld_89",
"PopAlexOld_39", "PopAlexOld_30", "PopAlexOld_33",
"PopZAC_1", "PopZAC_20", "PopZAC_21", "PopZAC_22", "PopZAC_23", "PopZAC_24",
"PopZAC_25", "PopZAC_26", "PopZAC_28", "PopZAC_7", "PopLON_11", "PopLON_14", "PopLON_15", "PopLON_17",
"PopLON_19", "PopLON_23", "PopLON_28", "PopLON_36", "PopLON_4", "PopLON_9", "PopPET_1", "PopPET_2",
"PopPET_3", "PopPET_4", "PopPET_5", "PopLAJ_13", "PopLAJ_16", "PopLAJ_17", "PopLAJ_19", "PopLAJ_20",
"PopLAJ_21", "PopLAJ_23", "PopLAJ_25", "PopLAJ_26", "PopLAJ_5", "PopSW_18", "PopSW_22", "PopSW_29",
"PopSW_2", "PopSW_31", "PopSW_34", "PopSW_36", "PopSW_3", "PopSW_45", "PopSW_5", "PopYED_1a",
"PopYED_1", "PopYED_2a", "PopYED_2", "PopYED_3a", "PopYED_3", "PopYED_4a", "PopYED_4", "PopYED_5a",
"PopYED_5", "PopSAM_1a", "PopSAM_1", "PopSAM_2a", "PopSAM_2", "PopSAM_3a", "PopSAM_3", "PopSAM_4",
"PopSAM_5a", "PopSAM_5" )
ordered_pop <- mergedAdmixTable[match(row_order, mergedAdmixTable$ID_code), ]
barNaming <- function(vec) {
retVec <- vec
for(k in 2:length(vec)) {
if(vec[k-1] == vec[k])
retVec[k] <- ""
}
return(retVec)
}
#----------------------------------
#5 Groups
ordered_pop$Pop1 <- as.factor(ordered_pop$Pop.x)
png("./Figures_data/Plots/Admix_5_bar.png", width = 6000, height = 3000)
# bottom, left, top, and right
par(mar=c(30,15,4,2))
barplot(t(as.matrix(ordered_pop[,c(3:7)])), col=map_colours_5g, border=NA,
names.arg=barNaming(ordered_pop$Pop1), las=2, cex.names=8, cex.axis=8)
dev.off()
####################################
# Plot Ancient and present day ancestry proportions
#Alex Old
Ancient_New_Alex <- ordered_pop[which(ordered_pop$Pop1=="AlexNew"|ordered_pop$Pop1=="AlexOld"),]
Ancient_New_Alex$ID_code <- as.factor(Ancient_New_Alex$ID_code)
orderedAncient = Ancient_New_Alex[order(Ancient_New_Alex$ID_code),]
orderedAncient$V99 <- orderedAncient$V10
png("./Figures_data/Plots/Admix_Ancient_bar5.png", width = 1000, height = 500)
barplot(t(as.matrix(orderedAncient[,c(3:7)])), col=map_colours_5g, border=NA,
names.arg=barNaming(orderedAncient$ID_code), las=2, cex.names=1.4)
dev.off()
#---------------------
# test if significant difference in proportions
# https://www.r-tutor.com/elementary-statistics/non-parametric-methods/mann-whitney-wilcoxon-test
# for Ellesmere (yellow)
wilcox.test(V2 ~ Pop.x, data=Ancient_New_Alex)
#W = 7, p-value = 0.00227
#alternative hypothesis: true location shift is not equal to 0
# for Alaska (blue)
wilcox.test(V5 ~ Pop.x, data=Ancient_New_Alex)
#W = 75, p-value = 0.004644
#alternative hypothesis: true location shift is not equal to 0
#-------------------
# barplot of difference for all V1-V5
gath_ancient <- gather(orderedAncient,"popGroup", "prob", V1:V5)
png("./Figures_data/Plots/Ancient_Admix_Amounts.png", width = 500, height = 500)
ggplot(data=gath_ancient, aes(x=Pop.x, y=prob))+
geom_boxplot(aes(x=Pop.x, y=prob, fill="blue"))+
facet_wrap(~popGroup, scales = "free")+
theme_classic()+
scale_fill_manual(values="blue")
#labs(fill="popGroup")
dev.off()
# plot V2
png("./Figures_data/Plots/Ancient_V2_Amount.png", width = 400, height = 300)
ggplot(data=orderedAncient, aes(x=Pop.x, y=V2))+
geom_boxplot(aes(x=Pop.x, y=V2, fill="yellow"))+
theme_classic()+
labs(y= "Europe/Ellesmere ancestry", x = "")+
theme(axis.text = element_text(size = 20), text = element_text(size = 20)) +
scale_fill_manual(values="yellow")
#labs(fill="popGroup")
dev.off()
# plot V5
png("./Figures_data/Plots/Ancient_V5_Amount.png", width = 400, height = 300)
ggplot(data=orderedAncient, aes(x=Pop.x, y=V5), cex=6)+
geom_boxplot(aes(x=Pop.x, y=V5, fill="deepskyblue"))+
theme_classic()+
labs(y= "Alaska ancestry", x = "")+
theme(axis.text = element_text(size = 20), text = element_text(size = 20)) +
scale_fill_manual(values="deepskyblue")
#labs(fill="popGroup")
dev.off()
#------------------------
#Ellesmere
Ancient_New_Alex <- ordered_pop[which(ordered_pop$Pop1=="AlexNew"|ordered_pop$Pop1=="AlexOld"|ordered_pop$Pop1=="EUR"|ordered_pop$Pop1=="AXE"|ordered_pop$Pop1=="PopFOS"|ordered_pop$Pop1=="GF"|ordered_pop$Pop1=="HAZ"|ordered_pop$Pop1=="SVN"|ordered_pop$Pop1=="SVO"),]
Ancient_New_Alex$ID_code <- as.factor(Ancient_New_Alex$ID_code)
orderedAncient = Ancient_New_Alex[order(Ancient_New_Alex$ID_code),]
png("./Figures_data/Plots/Admix_Ellesmere_bar5.png", width = 1000, height = 707)
barplot(t(as.matrix(orderedAncient[,c(3:7)])), col=map_colours_5g, border=NA,
names.arg=barNaming(orderedAncient$ID_code), las=2)
dev.off()
#####################################
# Admixture map - shifted to see all groups
#library(devtools)
#devtools::install_github("MikkoVihtakari/PlotSvalbard", upgrade = "never")
#library(PlotSvalbard)
# https://mikkovihtakari.github.io/PlotSvalbard/articles/PlotSvalbard_user_manual.html
#library(scatterpie)
# shifted coordinates
All_samples_data <- transform_coord(All_samples_data, lon = "Long_shift", lat = "Lat_shift", bind = TRUE, proj.og = "+proj=longlat +datum=WGS84", proj.out = "+init=epsg:3995")
All_samples_data$lon_shift.utm <- All_samples_data$lon.utm
All_samples_data$lat_shift.utm <- All_samples_data$lat.utm
All_samples_data <- subset(All_samples_data, select = -c(lon.utm, lat.utm))
# regular coordinates
All_samples_data <- transform_coord(All_samples_data, lon = "Long", lat = "Lat", bind = TRUE, proj.og = "+proj=longlat +datum=WGS84", proj.out = "+init=epsg:3995")
# make dataset of just what needs to be plotted
shift_Admix_lat_long <- subset(All_samples_data, select = c(ID_code, lon.utm, lat.utm, lon_shift.utm, lat_shift.utm, Pop, V1, V2, V3, V4, V5))
Pop <- c("V1", "V2", "V3", "V4", "V5")
png("./Figures_data/Plots/Admix_map5_take1_shiftedlat_long.png", width = 2000, height = 1700)
basemap("panarctic", limits=50) +
geom_scatterpie(aes(x = lon_shift.utm, y = lat_shift.utm, group = ID_code, r=200000), data = shift_Admix_lat_long, cols = Pop, size = 0.9) +
geom_point(aes(x = lon_shift.utm, y = lat_shift.utm), data = shift_Admix_lat_long, col="white", size=17) +
geom_point(aes(x = lon.utm, y = lat.utm), data = shift_Admix_lat_long, col="green", size=6) +
scale_fill_manual(values=map_colours_5g)+
geom_text(data = shift_Admix_lat_long, aes(x = lon_shift.utm, y = lat_shift.utm, label = Pop), color = "black", fontface = 2, size = 25.4/72.27*20)
dev.off()
#---------------------------------
# Averaged admixture map - not plotting each individual
Pop <- c("V1", "V2", "V3", "V4", "V5")
# map showing population structure makeup
png("./Figures_data/Plots/Admix_map5g_take1_avg.png", width = 2000, height = 1700)
basemap("panarctic", limits=50) +
geom_scatterpie(aes(x = lon.utm, y = lat.utm, group = Pop, r=200000), data = All_pop_data, cols = Pop, size = 0.5) +
scale_fill_manual(values=map_colours_5g)
dev.off()
#----------------------------
All_pop_data <- subset(All_pop_data, select = -c(lon.utm, lat.utm))
All_pop_data <- transform_coord(All_pop_data, lon = "Long_shift", lat = "Lat_shift", bind = TRUE, proj.og = "+proj=longlat +datum=WGS84", proj.out = "+init=epsg:3995")
All_pop_data$lon_shift.utm <- All_pop_data$lon.utm
All_pop_data$lat_shift.utm <- All_pop_data$lat.utm
All_pop_data <- subset(All_pop_data, select = -c(lon.utm, lat.utm))
All_pop_data <- transform_coord(All_pop_data, lon = "Long.x", lat = "Lat.x", bind = TRUE, proj.og = "+proj=longlat +datum=WGS84", proj.out = "+init=epsg:3995")
png("./Figures_data/Plots/Admix_map5_take1_shiftedlat_long_avg.png", width = 2000, height = 1700)
basemap("panarctic", limits=50) +
geom_scatterpie(aes(x = lon_shift.utm, y = lat_shift.utm, group = Pop, r=200000), data = All_pop_data, cols = Pop, size = 0.9) +
geom_point(aes(x = lon_shift.utm, y = lat_shift.utm), data = All_pop_data, col="white", size=17) +
geom_point(aes(x = lon.utm, y = lat.utm), data = All_pop_data, col="green", size=6) +
scale_fill_manual(values=map_colours_5g)+
geom_text(data = All_pop_data[-which(All_pop_data$Pop=="AlexOld"), ], aes(x = lon_shift.utm, y = lat_shift.utm, label = Pop), color = "black", fontface = 2, size = 25.4/72.27*20)
dev.off()
######################################
# Kluane
#check correlation with %BC and elevation
All_samples_data
Kluane <- All_samples_data[which(All_samples_data$Pop.x=="KL"|All_samples_data$Pop.x=="PC"),]
# Saximontana is V5 for Take 1
mod1 = lm(V3~Elevation, data = Kluane)
modsum = summary(mod1)
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 1.292e+00 1.440e-01 8.971 7.42e-08 ***
# Elevation -6.859e-04 9.243e-05 -7.421 1.00e-06 ***
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 0.07808 on 17 degrees of freedom
# Multiple R-squared: 0.7641, Adjusted R-squared: 0.7502
# F-statistic: 55.07 on 1 and 17 DF, p-value: 9.995e-07
png("./Figures_data/Plots/Elevation_BC_5.png", width = 3000, height = 1400)
par(mar=c(20,20,4,4))
plot(Kluane$Elevation, Kluane$V3, pch=20, cex = 10, mgp=c(10,5,0), cex.lab=5, cex.axis=5, xlab="Elevation(m)", ylab="BC Admixture Proportion")
abline(mod1)
dev.off()
orderedKluane = Kluane[order(Kluane$Elevation),]
barNaming <- function(vec) {
retVec <- vec
for(k in 2:length(vec)) {
if(vec[k-1] == vec[k])
retVec[k] <- ""
}
return(retVec)
}
png("./Figures_data/Plots/Admix_Kluane_bar5.png", width = 1000, height = 707)
barplot(t(as.matrix(orderedKluane[,c(3:7)])), col=map_colours_5g, border=NA,
names.arg=barNaming(orderedKluane$ID_code), las=2, cex.names=1.7)
dev.off()
#################################################
# Climate, ice and nucleotide diversity
# Icetime.x
# tavglong
# SumWindPi
All_pop_data_rmKL.PC.GEN <- All_pop_data[-which(All_pop_data$Pop=="KL"|All_pop_data$Pop=="GEN"|All_pop_data$Pop=="PC"),]
# Models
mod3 = lm(SumWindPi ~ tavglong, data = All_pop_data_rmKL.PC.GEN)
summary(mod3)
# adding in Kluane makes it significant
#Multiple R-squared: 0.1048, Adjusted R-squared: 0.07589
#F-statistic: 3.628 on 1 and 31 DF, p-value: 0.06614
# Multiple R-squared: 0.1152, Adjusted R-squared: 0.08918
# F-statistic: 4.427 on 1 and 34 DF, p-value: 0.04285
mod4 = lm(Ice_time.x ~ tavglong, data = All_pop_data_rmKL.PC.GEN)
summary(mod4)
#Multiple R-squared: 0.0237, Adjusted R-squared: -0.00419
#F-statistic: 0.8498 on 1 and 35 DF, p-value: 0.3629
# Multiple R-squared: 0.01962, Adjusted R-squared: -0.006184
# F-statistic: 0.7603 on 1 and 38 DF, p-value: 0.3887
mod5 = lm(SumWindPi ~ Ice_time.x, data = All_pop_data_rmKL.PC.GEN)
summary(mod5)
#Multiple R-squared: 0.3282, Adjusted R-squared: 0.3078
#F-statistic: 16.12 on 1 and 33 DF, p-value: 0.0003224
# Multiple R-squared: 0.1463, Adjusted R-squared: 0.1226
# F-statistic: 6.171 on 1 and 36 DF, p-value: 0.01777
#---------------------------------
# Plots
png("./Figures_data/Plots/WindPivsTemp_sub.png", width = 1000, height = 707)
plot(All_pop_data_rmKL.PC.GEN$SumWindPi ~ All_pop_data_rmKL.PC.GEN$tavglong, col="lightblue", pch=19, cex=2, xlab="Average Temperature (degrees Celsius)", ylab="Sites Pi")
abline(mod3, col="red", lwd=3)
text(SumWindPi ~ tavglong, labels=Pop, data=All_pop_data_rmKL.PC.GEN, cex=0.9, font=2)
dev.off()
png("./Figures_data/Plots/Ice_temp_sub.png", width = 1000, height = 707)
plot(All_pop_data_rmKL.PC.GEN$Ice_time.x ~ All_pop_data_rmKL.PC.GEN$tavglong, col="lightblue", pch=19, cex=2, xlab="Average Temperature (degrees Celsius)", ylab="Icetime")
abline(mod4, col="red", lwd=3)
text(Ice_time.x ~ tavglong, labels=Pop,data=All_pop_data_rmKL.PC.GEN, cex=0.9, font=2)
dev.off()
png("./Figures_data/Plots/IcetimesWIndPi_sub.png", width = 1000, height = 707)
plot(All_pop_data_rmKL.PC.GEN$SumWindPi ~ All_pop_data_rmKL.PC.GEN$Ice_time.x, col="lightblue", pch=19, cex=2, xlab="Ice retreat time (thousands of years)", ylab="Sites Pi")
abline(mod5, col="red", lwd=3)
text(SumWindPi ~ Ice_time.x, labels=Pop, data=All_pop_data_rmKL.PC.GEN, cex=0.9, font=2)
dev.off()
#--------------------------
# total data
# check site and window pi correlate
png("./Figures_data/Plots/SitevsWIndPi.png", width = 1000, height = 707)
plot(All_pop_data$SumWindPi ~ All_pop_data$SumSitePi, pch=20, xlab="Sites Pi", ylab="Window Pi")
dev.off()
# Models
mod3 = lm(SumWindPi ~ tavglong, data = All_pop_data)
summary(mod3)
# Residuals:
# Min 1Q Median 3Q Max
# -14.2604 -1.5667 0.1728 1.9179 11.5762
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 16.8080 1.7931 9.374 5.95e-11 ***
# tavglong 0.5029 0.2390 2.104 0.0428 *
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 4.205 on 34 degrees of freedom
# (6 observations deleted due to missingness)
# Multiple R-squared: 0.1152, Adjusted R-squared: 0.08918
# F-statistic: 4.427 on 1 and 34 DF, p-value: 0.04285
mod4 = lm(Ice_time.x ~ tavglong, data = All_pop_data)
summary(mod4)
# Residuals:
# Min 1Q Median 3Q Max
# -9.730 -2.978 -1.069 1.762 8.380
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 10.9930 1.5866 6.928 3.07e-08 ***
# tavglong 0.1624 0.1863 0.872 0.389
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 4.565 on 38 degrees of freedom
# (2 observations deleted due to missingness)
# Multiple R-squared: 0.01962, Adjusted R-squared: -0.006184
# F-statistic: 0.7603 on 1 and 38 DF, p-value: 0.3887
mod5 = lm(SumWindPi ~ Ice_time.x, data = All_pop_data)
summary(mod5)
# Residuals:
# Min 1Q Median 3Q Max
# -11.3903 -1.5145 0.0607 1.0588 14.5881
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 15.7977 1.8528 8.526 3.66e-10 ***
# Ice_time.x 0.3520 0.1417 2.484 0.0178 *
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 4.082 on 36 degrees of freedom
# (4 observations deleted due to missingness)
# Multiple R-squared: 0.1463, Adjusted R-squared: 0.1226
# F-statistic: 6.171 on 1 and 36 DF, p-value: 0.01777
#---------------------------------
# Plots
png("./Figures_data/Plots/WindPivsTemp.png", width = 1000, height = 707)
plot(All_pop_data$SumWindPi ~ All_pop_data$tavglong, col="lightblue", pch=19, cex=2, xlab="Average Temperature (degrees Celsius)", ylab="Sites Pi")
abline(mod3, col="red", lwd=3)
text(SumWindPi ~ tavglong, labels=Pop, data=All_pop_data, cex=0.9, font=2)
dev.off()
png("./Figures_data/Plots/Ice_temp.png", width = 1000, height = 707)
plot(All_pop_data$Ice_time.x ~ All_pop_data$tavglong, col="lightblue", pch=19, cex=2, xlab="Average Temperature (degrees Celsius)", ylab="Icetime")
abline(mod4, col="red", lwd=3)
text(Ice_time.x ~ tavglong, labels=Pop,data=All_pop_data, cex=0.9, font=2)
dev.off()
png("./Figures_data/Plots/IcetimesWIndPi.png", width = 1000, height = 707)
plot(All_pop_data$SumWindPi ~ All_pop_data$Ice_time.x, col="lightblue", pch=19, cex=2, xlab="Ice retreat time (thousands of years)", ylab="Sites Pi")
abline(mod5, col="red", lwd=3)
text(SumWindPi ~ Ice_time.x, labels=Pop, data=All_pop_data, cex=0.9, font=2)
dev.off()
#------------------------------------
# no Beringia
mod6 = lm(SumWindPi ~ Ice_time2, data = All_pop_data_rmKL.PC.GEN)
summary(mod6)
# no saximontana
#Multiple R-squared: 0.1073, Adjusted R-squared: 0.07161
#F-statistic: 3.005 on 1 and 25 DF, p-value: 0.0953
#--
mod6 = lm(SumWindPi ~ Ice_time2, data = All_pop_data)
summary(mod6)
# total
# Multiple R-squared: 0.09815, Adjusted R-squared: 0.06594
# F-statistic: 3.047 on 1 and 28 DF, p-value: 0.09184
##################
# make plots of Pi, Tajimas D
# order population averages by longitude
All_pop_data_ord <- All_pop_data[order(All_pop_data$Long.x),]
# remove Sverdrup New
All_pop_data_ord_sub <- All_pop_data_ord[-which(All_pop_data_ord$Pop=="SVN"),]
# remove NA values
All_pop_data_ord_sub <- All_pop_data_ord_sub[-which(is.na(All_pop_data_ord_sub$SumWindPi)),]
# make Pop a factor
All_pop_data_ord_sub$Pop <- as.factor(All_pop_data_ord_sub$Pop)
# plot Nucleotide diversity
png("./Figures_data/Plots/AvgPi_noSverdrup.png", width = 2200, height = 707)
plot(All_pop_data_ord_sub$Pop, All_pop_data_ord_sub$SumWindPi)
dev.off()
png("./Figures_data/Plots/AvgTajima_noSverdrup.png", width = 1700, height = 707)
plot(All_pop_data_ord_sub$Pop, All_pop_data_ord_sub$TajimaAvg, cex.axis=2, cex.lab=1.5, las=2)
dev.off()
##################
# make barplots of heterozygotsity with per individual data
# All_samples_data$F, All_samples_data$O.HOM. , All_samples_data$E.HOM.
All_samples_data$Pop <- as.factor(All_samples_data$Pop)
# png("./Figures_data/Plots/FIS.png", width = 2200, height = 707)
# plot(All_samples_data$Pop, All_samples_data$F, col="blue")
# dev.off()
png("./Figures_data/Plots/Obser_Homozygosity.png", width = 2200, height = 707)
plot(All_samples_data$Pop, All_samples_data$O.HOM., col="blue")
dev.off()
png("./Figures_data/Plots/Exp_Homozygosity.png", width = 2200, height = 707)
plot(All_samples_data$Pop, All_samples_data$E.HOM., col="blue")
dev.off()
#-------
# order by longitude
Het_data <- as.data.frame(cbind(All_samples_data$Pop.x, All_samples_data$F, All_samples_data$Long, All_samples_data$map_colours_5g))
colnames(Het_data) <- c("Pop", "F", "Long", "map_colours_5g")
Het_data$F <- as.numeric(Het_data$F)
Het_data$Long <- as.numeric(Het_data$Long)
Het_data_ord <- Het_data[order(Het_data$Long),]
Het_data_ord$Pop <- as.factor(Het_data_ord$Pop)
levels(Het_data_ord$Pop)
Het_data_ord$Pop <- factor(Het_data_ord$Pop, levels = c("GEN", "ATQ", "BARD", "MAT", "MNT", "IMN", "SAG" , "DEN", "MIL", "QHI", "KL", "PC", "PEA",
"KUQ", "YAM", "AXE", "EUR", "FOS", "GF", "SVO" , "BY" , "AlexNew", "AlexOld" , "HAZ", "Iq",
"CR", "Kik", "DLG", "DQG", "025", "IG", "ZAC", "LON", "PET", "LAJ", "SW", "SAM", "YED"))
Het_cols <- c("purple", "deepskyblue", "deepskyblue", "deepskyblue", "deepskyblue", "deepskyblue", "deepskyblue" , "deepskyblue", "deepskyblue", "deepskyblue", "deepskyblue", "deepskyblue", "deepskyblue",
"deepskyblue", "deepskyblue", "deepskyblue", "yellow", "yellow", "yellow", "yellow" , "yellow" , "yellow", "yellow" , "yellow", "orange",
"orange", "orange", "orange", "orange", "orange", "orange", "yellow", "yellow", "yellow", "yellow", "yellow", "red", "red")
# png("./Figures_data/Plots/FIS_ordered.png", width = 2200, height = 707)
# plot(Het_data_ord$Pop, Het_data_ord$F, col=Het_cols)
# dev.off()
# par(mar = c(bottom, left, top, right))
png("./Figures_data/Plots/Fis_col_box.png", width = 4000, height = 1700)
par(mar= c(20,15,4,4))
plot(F ~ Pop, data = Het_data_ord, xlab="", ylab="", cex=3, cex.lab=6, cex.axis=5, las=2, col = Het_cols)
stripchart(F ~ Pop, vertical = TRUE, method = "jitter", pch = 16,las = 2, col = "blue", cex = 1.5, data = Het_data_ord, add=TRUE)
lines(Het_data_ord$Pop, y=rep(0, length(Het_data_ord$Pop)), lwd=3)
dev.off()
########################################
# Population average maps
# Climate maps
png("./Figures_data/Plots/Tempavglong_map.png", width = 3000, height = 2700)
basemap("panarctic", limits=45) +
geom_point(aes(x = lon.utm, y = lat.utm, colour= tavglong), data = All_pop_data, size=30) +
scale_color_gradient (low="blue", high="red")+
theme(legend.key.height= unit(10, 'cm'),
legend.key.width= unit(20, 'cm'),
legend.text = element_text(size=80))
dev.off()
png("./Figures_data/Plots/Precip_long_map.png", width = 3000, height = 2700)
basemap("panarctic", limits=45) +
geom_point(aes(x = lon.utm, y = lat.utm, colour= prcplong), data = All_pop_data, size=30) +
scale_color_gradient (low="blue", high="red")+
theme(legend.key.height= unit(10, 'cm'),
legend.key.width= unit(20, 'cm'),
legend.text = element_text(size=80))
dev.off()
#-----------------------
# cut Sverdrup Old/New and Outgroup
All_pop_data_sub <- All_pop_data[-which(All_pop_data$Pop=="SVN"|All_pop_data$Pop=="SVO"|All_pop_data$Pop=="MER"|All_pop_data$Pop=="HAR"|All_pop_data$Pop=="PHE"),]
# Window Pi Measure
png("./Figures_data/Plots/WindPi_map_noSverdrup.png", width = 3000, height = 2700)
basemap("panarctic", limits=45) +
geom_point(aes(x = lon.utm, y = lat.utm, colour= SumWindPi), data = All_pop_data_sub[-which(All_pop_data_sub$Pop=="GEN"), ], size=30)+
scale_color_gradient2(midpoint=25, low="lightblue", mid="yellow", high="red", space ="Lab" ) +
theme(legend.key.height= unit(10, 'cm'),
legend.key.width= unit(10, 'cm'),
legend.text = element_text(size=100))
dev.off()
# Tajima's D
png("./Figures_data/Plots/Tajima_map_sub.png", width = 3000, height = 2700)
basemap("panarctic", limits=45) + geom_point(aes(x = lon.utm, y = lat.utm, colour= TajimaAvg), data = All_pop_data_sub, size=30) +
scale_color_gradient2(midpoint=0, low="blue", mid="white", high="red", space ="Lab" )+
theme(legend.key.height= unit(10, 'cm'),
legend.key.width= unit(10, 'cm'),
legend.text = element_text(size=80))
dev.off()
# map of leaf weights
png("./Figures_data/Plots/Leaf_Weight_map.png", width = 3000, height = 2700)
basemap("panarctic", limits=45) +
geom_point(aes(x = lon.utm, y = lat.utm, colour=Leaf_weight), data = All_pop_data_sub, size=30) +
scale_color_gradient2(midpoint=1.0, low="blue", mid="white", high="red", space ="Lab" )+
theme(legend.key.height= unit(10, 'cm'),
legend.key.width= unit(10, 'cm'),
legend.text = element_text(size=80))
dev.off()
# deglaciation time
png("./Figures_data/Plots/icetime.png", width = 3000, height = 2700)
basemap("panarctic", limits=45) + geom_point(aes(x = lon.utm, y = lat.utm, colour= Ice_time.x), data = All_pop_data_sub, size=30) +
scale_color_gradient2(midpoint=5, low="red", mid="yellow", high="blue", space ="Lab" )+
theme(legend.key.height= unit(10, 'cm'),
legend.key.width= unit(10, 'cm'),
legend.text = element_text(size=80))
dev.off()
# deglaciation time
png("./Figures_data/Plots/icetime.png", width = 3000, height = 2700)
basemap("panarctic", limits=45) + geom_point(aes(x = lon.utm, y = lat.utm, colour= Ice_time.x), data = All_pop_data_sub, size=30) +
scale_color_gradient2(midpoint=5, low="red", mid="yellow", high="blue", space ="Lab" )+
theme(legend.key.height= unit(10, 'cm'),
legend.key.width= unit(10, 'cm'),
legend.text = element_text(size=80))
dev.off()
# FIS map
png("./Figures_data/Plots/Inbreeding_FIS_Map.png", width = 3000, height = 2700)
basemap("panarctic", limits=45) + geom_point(aes(x = lon.utm, y = lat.utm, colour= FIS), data = All_pop_data_sub, size=30) +
scale_color_gradient2(midpoint=-0.4, low="red", mid="yellow", high="blue", space ="Lab" )+
theme(legend.key.height= unit(10, 'cm'),
legend.key.width= unit(10, 'cm'),
legend.text = element_text(size=80))
dev.off()
# lots of heterozygotsity - maybe due to different contigs mapping to same location, fixed by filtering out SNPs that were 60%+ heterozygous