Comparison_GO_predictions_Taxus_final_results
Thomas Francisco
2024-11-11
The aim of this script is to compare population genomic offset predictions across models and sets of SNPs. It will also produce a map of the genomic offsets described in the paper.
meta_data_pop <- read.csv("C:/Users/tfrancisco/Documents/Thesis/Data/Species/Taxus_baccata/Populations/taxus_sample_29pop.csv",h=T,sep=";",dec=",")
#alphabetic order
meta_data_pop_order <- meta_data_pop[order(meta_data_pop$Population),]Data
list_models <- c("all","random_same_AF","LC","random_AF_V2","all_outliers")
list_period <- c("present","future_mean")
df_present <- list()
df_future <- list()
# Loop through the models and periods, loading the data and extracting the second column
for (x in 1:length(list_models)) {
name <- list_models[x]
for (i in 1:length(list_period)) {
period <- list_period[i]
# Load RDA data (standard GO)
load(paste0("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/RDA/data/GO_T_Adapcon_Gentree_RDA_", name, "_", period, ".Rdata"))
df <- get(paste0("GO_T_Adapcon_Gentree_RDA_",name, "_", period))
temp_df <- data.frame(temp = df[,2]) # Extract the second column
colnames(temp_df) <- paste0("RDA_",name) # Rename the column
# Append to the corresponding list based on the period
if (period == "present") {
df_present[[length(df_present) + 1]] <- temp_df
} else if (period == "future_mean") {
df_future[[length(df_future) + 1]] <- temp_df
}
# Load GF GO data
load(paste0("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/GF/data/GCMs_separate/GO_T_Adapcon_Gentree_GF_", name, "_", period, ".Rdata"))
df <- get(paste0("GO_T_Adapcon_Gentree_GF_",name, "_", period))
temp_df <- data.frame(temp = df[,2]) # Extract the second column
colnames(temp_df) <- paste0("GF_",name) # Rename the column
if (period == "present") {
df_present[[length(df_present) + 1]] <- temp_df
} else if (period == "future_mean") {
df_future[[length(df_future) + 1]] <- temp_df
}
}
}
# Combine all data frames in the lists by column for each period
df_present_combined <- do.call(cbind, df_present)
df_present_combined <- df_present_combined[, order(colnames(df_present_combined))]
df_future_combined <- do.call(cbind, df_future)
df_future_combined <- df_future_combined[, order(colnames(df_future_combined))]
#df_future_combined$Population <- GO_T_Adapcon_Gentree_GF_all_future_mean$Population
# Optionally, save the combined data frames to CSV or RData files
write.csv(df_present_combined, "present_combined.csv", row.names = FALSE)
write.csv(df_future_combined, "future_combined.csv", row.names = FALSE)
# Save as RData
#save(df_present_combined, file = "present_combined.RData")
#save(df_future_combined, file = "future_combined.RData")Pearson correlation
reduce set paper (random AF, all, outlier)
correlation_present <- cor(df_present_combined)
correlation_present_order <- correlation_present[c(2,3,1,5,6,4),c(2,3,1,5,6,4)]
colnames(correlation_present_order) <- c("GF_outlier","GF_random","GF_all","RDA_outlier","RDA_random","RDA_all")
row.names(correlation_present_order) <- c("GF_outlier","GF_random","GF_all","RDA_outlier","RDA_random","RDA_all")
corrplot(correlation_present_order, method = "number", addrect = 2, col = c("darkorange","darkred"), type = "lower", tl.col = "black", tl.cex = 0.6, number.cex = 0.6)
correlation_future <- cor(df_future_combined)
correlation_future_order <- correlation_future[c(3,5,1,8,10,6),c(3,5,1,8,10,6)]
colnames(correlation_future_order) <- c("GF_outlier","GF_random","GF_all","RDA_outlier","RDA_random","RDA_all")
row.names(correlation_future_order) <- c("GF_outlier","GF_random","GF_all","RDA_outlier","RDA_random","RDA_all")
corrplot(correlation_future_order, method = "number", addrect = 2, col = c("darkorange","darkred"), type = "lower", tl.col = "black", tl.cex = 1, number.cex = 1)
pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/comparison/correlation_GO_present_values_GF_RDA_reduce_paper.pdf");corrplot(correlation_present_order, method = "number", addrect = 2, col = c("darkorange","darkred"), type = "lower", tl.col = "black", tl.cex = 0.6, number.cex = 0.6);dev.off()## png
## 2pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/comparison/correlation_GO_future_values_GF_RDA_reduce_paper.pdf");corrplot(correlation_future_order, method = "number", addrect = 2, col = c("darkorange","darkred"), type = "lower", tl.col = "black", tl.cex = 1, number.cex = 1);dev.off()## png
## 2All the sets
correlation_future <- cor(df_future_combined)
correlation_future_order_allset <- correlation_future[c(1,2,3,4,5,6,7,8,9,10),c(1,2,3,4,5,6,7,8,9,10)]
correlation_future_order_allset <- correlation_future[c(5,4,1,2,3,10,9,6,7,8),c(5,4,1,2,3,10,9,6,7,8)]
colnames(correlation_future_order_allset) <- c("GF_random","GF_random_2","GF_all","GF_all_outlier","GF_outlier","RDA_random","RDA_random_2","RDA_all","RDA_all_outlier","RDA_outlier")
row.names(correlation_future_order_allset) <- c("GF_random","GF_random_2","GF_all","GF_all_outlier","GF_outlier","RDA_random","RDA_random_2","RDA_all","RDA_all_outlier","RDA_outlier")
corrplot(correlation_future_order_allset, method = "number", addrect = 2, col = c("darkorange","darkred"), type = "lower", tl.col = "black", tl.cex = 0.6, number.cex = 0.6)
pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/comparison/correlation_new_modelsV2.pdf");corrplot(correlation_future_order_allset, method = "number", addrect = 2, col = c("darkorange","darkred"), type = "lower", tl.col = "black", tl.cex = 0.6, number.cex = 0.6);dev.off()## png
## 2pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/comparison/correlation_GO_future_values_GF_RDA_allsets.pdf");corrplot(correlation_future_order_allset, method = "number", addrect = 2, col = c("darkorange","darkred"), type = "lower", tl.col = "black", tl.cex = 1, number.cex = 1);dev.off()## png
## 2Comparison GO rank
GO_T_Adapcon_Gentree_GF_outlier_future_mean <- GO_T_Adapcon_Gentree_GF_LC_future_mean
GO_T_Adapcon_Gentree_RDA_outlier_future_mean <- GO_T_Adapcon_Gentree_RDA_LC_future_mean
GO_T_Adapcon_Gentree_GF_random_future_mean <- GO_T_Adapcon_Gentree_GF_random_same_AF_future_mean
GO_T_Adapcon_Gentree_RDA_random_future_mean <- GO_T_Adapcon_Gentree_RDA_random_same_AF_future_mean
# List of SNP sets
#list_set <- c("all", "random", "random_same_AF", "LC", "MC", "CG")
list_set <- c("GF_outlier","GF_random","GF_all","RDA_outlier","RDA_random","RDA_all")
# Initialize an empty list to store data for all SNP sets
plot_data <- list()
# Load the RColorBrewer package for dynamic color palettes
library(RColorBrewer)
color_palette <- c("#E69F00", # Orange
"#56B4E9", # Sky Blue
"#009E73", # Green
"#F0E442", # Yellow
"#0072B2", # Blue
"#D55E00", # Red
"#CC79A7") # Pink
ranks_df <- data.frame()
all_top_populations <- character(0)
meta_data_pop$pop_abbrev <- c("VIS","BAU","SDF","PAT","CHO","VOU","OLY","ANC","PDT","UMB","VAL","BRA","HAR","GOR","BOM","UNS","BUJA","BUJG","CAR","RAS","SUE","SAV","BLI","HHR","JUR","WAL","CED","RWM","YEW")
meta_data_f <- meta_data_pop[order(meta_data_pop$Population),]
# Loop over each climatic model
for (i in 1:length(list_set)) {
set <- list_set[i]
# Get the genomic offset data for the current SNP set and model
GO_data <- get(paste0("GO_T_Adapcon_Gentree_", set, "_future_mean"))
colnames(GO_data) <- c("Population", "GO")
# Rank the populations by genomic offset (highest GO gets rank 1)
GO_data$Rank <- rank(-GO_data$GO, ties.method = "first")
# Add columns for the climatic model and SNP set
GO_data$Set <- set
# Append to ranks dataframe
ranks_df <- rbind(ranks_df, GO_data)
# Find the top (rank 1) and bottom (rank 29) populations
top_population <- GO_data %>% filter(Rank == 1) %>% pull(Population)
#bottom_population <- GO_data %>% filter(Rank == max(GO_data$Rank)) %>% pull(Population)
# Store unique top and bottom populations
all_top_populations <- unique(c(all_top_populations, top_population))#bottom_population
}
# Adjust the number of colors if needed
num_top_populations <- length(all_top_populations)
if (num_top_populations > length(color_palette)) {
color_palette <- colorRampPalette(brewer.pal(9, "Set1"))(num_top_populations)
}
# Assign a unique color to each of the selected populations (both top and bottom)
population_colors <- setNames(color_palette[1:num_top_populations], all_top_populations)
# Create a new column to store color assignments (highlight only rank 1 and rank 29 populations)
ranks_df$Highlight <- ifelse(ranks_df$Population %in% all_top_populations, ranks_df$Population, "Other")
# Map colors for top & bottom populations and grey for the rest
ranks_df$Color <- ifelse(ranks_df$Highlight == "Other", "grey", population_colors[ranks_df$Highlight])
# Store the data frame for this SNP set in the plot_data list
plot_data[[set]] <- ranks_df
# Generate the plot for this SNP set
p <- ggplot(ranks_df, aes(x = Set, y = Rank, group = Population, color = Color)) +
geom_line(alpha = 0.8, size = 1.25) +
geom_point(size = 2) +
geom_text(
data = ranks_df %>% filter(Set == list_set[5]), # Label only in first column
aes(label = meta_data_f$pop_abbrev[match(Population, meta_data_f$Population)]),
hjust = 1, # Align to the left
vjust = 0,
size = 3.8, # Increase text size
nudge_x = +0.50,
fontface= "italic",color="black"
# Move text further left
) +
scale_y_reverse() + # Reverse rank (1 = top)
scale_color_identity() + # Use assigned colors
labs(
title = "Comparison Genomic Offset Predictions Across Models",
x = "Genomic Offset Models", y = "Population Rank"
) +
theme_minimal() +
theme(
legend.position = "none",
panel.grid.minor = element_blank(),
strip.text = element_text(size = 10, face = "bold"),
legend.title = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 12),
legend.text = element_text(size = 12)
)
print(p) # Display plot
#save
pdf(paste0("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/comparison/GO_rank_comparison_across_models.pdf"));print(p);dev.off()## png
## 2Including bottom populations
color_palette <- c("#E69F00", # Orange
"#56B4E9", # Sky Blue
"#009E73", # Green
"#F0E442", # Yellow
"#0072B2", # Blue
"#D55E00", # Red
"#CC79A7") # Pink
color_palette <- c("#0072B2","#CC79A7") # Orange
ranks_df <- data.frame()
all_top_populations <- character(0)
# Loop over each climatic model
for (i in 1:length(list_set)) {
set <- list_set[i]
# Get the genomic offset data for the current SNP set and model
GO_data <- get(paste0("GO_T_Adapcon_Gentree_", set, "_future_mean"))
colnames(GO_data) <- c("Population", "GO")
# Rank the populations by genomic offset (highest GO gets rank 1)
GO_data$Rank <- rank(-GO_data$GO, ties.method = "first")
# Add columns for the climatic model and SNP set
GO_data$Set <- set
# Append to ranks dataframe
ranks_df <- rbind(ranks_df, GO_data)
# Find the top (rank 1) and bottom (rank 29) populations
top_population <- GO_data %>% filter(Rank == 1) %>% pull(Population)
bottom_population <- GO_data %>% filter(Rank == max(GO_data$Rank)) %>% pull(Population)
# Store unique top and bottom populations
all_top_populations <- unique(c(all_top_populations, top_population, bottom_population))
}
# Adjust the number of colors if needed
num_top_populations <- length(bottom_population)
if (num_top_populations > length(color_palette)) {
color_palette <- colorRampPalette(brewer.pal(9, "Set1"))(num_top_populations)
}
#Castle_Eden_Dene
# Assign a unique color to each of the selected populations (both top and bottom)
keep_pop <- c("Castle_Eden_Dene","Hornli-Hulftegg-Roten")
population_colors <- setNames(color_palette[1:2], keep_pop)
# Create a new column to store color assignments (highlight only rank 1 and rank 29 populations)
ranks_df$Highlight <- ifelse(ranks_df$Population %in% keep_pop, ranks_df$Population, "Other")
# Map colors for top & bottom populations and grey for the rest
ranks_df$Color <- ifelse(ranks_df$Highlight == "Other", "grey", population_colors[ranks_df$Highlight])
# Store the data frame for this SNP set in the plot_data list
plot_data[[set]] <- ranks_df
# Generate the plot for this SNP set
p <- ggplot(ranks_df, aes(x = Set, y = Rank, group = Population, color = Color)) +
geom_line(alpha = 0.8, size = 1.25) +
geom_point(size = 2) +
scale_y_reverse() + # Reverse rank (1 = top)
scale_color_identity() + # Use assigned colors
labs(
title = paste("RDA Rank Variability for SNP Set:", set),
x = "Climatic Models", y = "Population Rank"
) +
theme_minimal() +
theme(
legend.position = "none", # Remove legend
panel.grid.minor = element_blank(),
strip.text = element_text(size = 10, face = "bold"),
legend.title = element_text(size = 10)
)
print(p) # Display plot
#save
pdf(paste0("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/comparison/GO_rank_similarity.pdf"));print(p);dev.off()## png
## 2GO map
Map biplot without low trust
Based on the evaluation of the predictions using phenotypic data, we concluded that GF and RDA predictions using a set of loci with random same-allele frequencies should be used to predict genomic offset. Only predictions for populations where the two methods agree would be interpreted.
df_two_GO <- data.frame(Population=GO_T_Adapcon_Gentree_GF_all_future_mean[,1],df_future_combined[,c(3,6)])
df_two_GO_outliers <- data.frame(Population=GO_T_Adapcon_Gentree_GF_all_future_mean[,1],df_future_combined[,c(3,8)])
df_two_GO_random_same_AF <- data.frame(Population=GO_T_Adapcon_Gentree_GF_all_future_mean[,1],df_future_combined[,c(5,10)])
df_two_GO_random_same_AFV2 <- data.frame(Population=GO_T_Adapcon_Gentree_GF_all_future_mean[,1],df_future_combined[,c(4,9)])
df_two_GO_all_outliers <- data.frame(Population=GO_T_Adapcon_Gentree_GF_all_future_mean[,1],df_future_combined[,c(2,7)])We can plot the two GO for each population using two different colors:
list_df_combined <- c("df_two_GO_outliers","df_two_GO_random_same_AF","df_two_GO_random_same_AFV2","df_two_GO_all_outliers")
list_names <- c("outlier","random_AF","random_AF2","all_outliers")
for(x in 1:length(list_df_combined)){
df <- get(list_df_combined[x])
name <- list_names[x]
Genomic_offset_combined <- merge(df,meta_data_pop_order[,c(2,4,5)],"Population")
# DataFrame containing GO values for two models (RDA and GF)
colnames(Genomic_offset_combined) <- c("Population", "GO_GF", "GO_RDA","Latitude", "Longitude")
# Convert Longitude and Latitude to numeric
Genomic_offset_combined <- Genomic_offset_combined %>%
mutate(Longitude = as.numeric(Longitude), Latitude = as.numeric(Latitude))
colors <- c("darkgreen", "#FDF7B3", "#FC4E2A", "#BD0026", "darkorchid4")
admin <- ne_countries(scale = "medium", returnclass = "sf")
#scale the values to have comparable classes
#cut_interval
Genomic_offset_combined$scaled_GO_RDA <- (Genomic_offset_combined$GO_RDA-min(Genomic_offset_combined$GO_RDA))/(max(Genomic_offset_combined$GO_RDA)-min(Genomic_offset_combined$GO_RDA))
# Create interval categories for `GO_GF` and `GO_RDA`
Genomic_offset_combined <- Genomic_offset_combined %>%
mutate(GO_GF_interval = cut_interval(GO_GF, n = 5, labels = FALSE),
GO_RDA_interval = cut_interval(GO_RDA, n = 5, labels = FALSE))
# Convert intervals to factor for consistent color mapping
Genomic_offset_combined$GO_GF_interval <- as.factor(Genomic_offset_combined$GO_GF_interval)
Genomic_offset_combined$GO_RDA_interval <- as.factor(Genomic_offset_combined$GO_RDA_interval)
# Plot with vertical split for the half-circles
plot <- ggplot(data = Genomic_offset_combined) +
geom_sf(data = admin, fill = gray(0.92), size = 0) +
geom_sf(data = admin, fill = NA, size = 0.1) +
coord_sf(xlim = c(-10, 30), ylim = c(35, 62), expand = FALSE) +
# Create half-circle markers with a vertical split by rotating the start and end angles
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = -pi/2, end = pi/2, fill = GO_GF_interval # Left half for GO_GF
), color = "black", data = Genomic_offset_combined) +
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = pi/2, end = 3 * pi/2, fill = GO_RDA_interval # Right half for GO_RDA
), color = "black", data = Genomic_offset_combined) +
# Set fill colors and map details for intervals
scale_fill_manual(
values = colors,
labels = c("low values", "", "", "", "high values"),
drop = FALSE, na.translate = FALSE
) +
xlab("Longitude") + ylab("Latitude") +
ggtitle("Genomic offset across populations") +
theme_bw(base_size = 11) +
theme(
legend.position = "right", panel.grid = element_blank(),
strip.text = element_text(size = 11),
plot.title = element_text(hjust = 0.5, color = "Black", face = "italic")
)
# Print the plot
print(plot)
assign(paste0("Genomic_offset_combined_",name),Genomic_offset_combined)
}
#pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/Final_figures/GO_RDA_across_populations.pdf");print(plot);dev.off()Indicating low trust population
Based on these results, we decided to remove the populations for which predictions were inconsistent across methods, and to calculate the elevation relationship using only the GF predictions (as GF was more strongly associated with fitness than RDA).
Plot of GO highlighting the populations we are less trusting of.
At least two categorical of difference for random same AF
Bohinj_Obrne_Mokri_Log, Fonte tassete Prati, Rascafria, Sainte baume and Serra di fiurmaba
for(x in 1:length(list_names)){
df <- get(paste0("Genomic_offset_combined_",list_names[x]))
Genomic_offset_combined_MC <- df[-c(9,19,21,22,23),]
Genomic_offset_combined_MC_elev <- merge(Genomic_offset_combined_MC,meta_data_pop_order[,c(2,6)],"Population")
Genomic_offset_combined_MC_elev$Elevation.DEM_90m.<- as.numeric(Genomic_offset_combined_MC_elev$Elevation.DEM_90m.)
Genomic_offset_combined_MC_elev$Latitude<- as.numeric(Genomic_offset_combined_MC_elev$Latitude)
cor(Genomic_offset_combined_MC_elev$GO_GF,Genomic_offset_combined_MC_elev$Latitude)
}
Genomic_offset_combined_random_AF## Population GO_GF GO_RDA Latitude Longitude
## 1 Ancona 0.020504063 0.06891124 43.35068 13.1988917
## 2 Blindtal 0.017416033 0.07302145 46.35519 7.9903570
## 3 Bohinj_Obrne_Mokri_Log 0.014918901 0.04431264 46.31436 14.0550770
## 4 Brandvik_Stord 0.006118212 0.04477659 59.85280 5.6104660
## 5 Bujaruelo-ADAPCON 0.021863894 0.07461664 42.70098 -0.1165162
## 6 Bujaruelo-GENTREE-1 0.022266494 0.07496925 42.67478 -0.1232048
## 7 Cardo 0.017906489 0.07347198 40.95839 0.5876957
## 8 Castle_Eden_Dene 0.004919315 0.04443507 54.74464 -1.3344239
## 9 Fonte_tassete_Prati_di_Tivo 0.024241671 0.05447067 42.50618 13.5659382
## 10 Foresta_Umbra 0.011806892 0.05247863 41.80630 15.9830000
## 11 Gorenja_zaga 0.016022083 0.06661883 45.51882 14.9112200
## 12 Harmanec 0.010292957 0.03305003 48.83560 19.0132000
## 13 Hornli-Hulftegg-Roten 0.013521115 0.05695896 47.30408 8.9493621
## 14 Jura 0.012802252 0.04497744 47.24159 7.5183380
## 15 Mt_Cholomon 0.017928249 0.06570500 40.43800 23.5401333
## 16 Mt_Olympus 0.021029936 0.07122069 40.09367 22.4290893
## 17 Mt_Vourinos 0.019865806 0.07769358 40.19240 21.6612167
## 18 Paterzeller_Eibenwald 0.012790435 0.04707325 47.85975 11.0499052
## 19 Rascafria 0.031739862 0.05892408 40.82293 -3.9001348
## 20 Roudsea_Wood_and_Mosses 0.004767286 0.03691034 54.23103 -3.0246412
## 21 Sainte_Baume 0.008443698 0.09282830 43.32711 5.7549626
## 22 Saro_Vasterskog 0.013998021 0.07187861 57.50521 11.9249252
## 23 Serra_di_Fiumorbu 0.013836414 0.07060059 41.95458 9.2729608
## 24 Sueve 0.005769873 0.03347212 43.43846 -5.2323694
## 25 Unska_kolisevka 0.017662244 0.06117289 45.81784 14.2698200
## 26 Valditacca 0.016026290 0.06861820 44.38140 10.0847000
## 27 Visegrad 0.016456096 0.05957957 43.77139 19.2508330
## 28 Wallis 0.017565161 0.06370346 46.32642 7.5875970
## 29 Yewbarrow 0.004790029 0.03630962 54.28004 -3.0072634
## scaled_GO_RDA GO_GF_interval GO_RDA_interval
## 1 0.599903763 3 3
## 2 0.668661434 3 4
## 3 0.188406480 2 1
## 4 0.196167665 1 1
## 5 0.695346567 4 4
## 6 0.701245214 4 4
## 7 0.676198097 3 4
## 8 0.190454590 1 1
## 9 0.358334849 4 2
## 10 0.325011050 2 2
## 11 0.561555186 3 3
## 12 0.000000000 2 1
## 13 0.399960335 2 2
## 14 0.199527553 2 1
## 15 0.546268335 3 3
## 16 0.638537460 4 4
## 17 0.746818995 3 4
## 18 0.234587352 2 2
## 19 0.432833712 5 3
## 20 0.064577132 1 1
## 21 1.000000000 1 5
## 22 0.649543420 2 4
## 23 0.628164028 2 4
## 24 0.007060966 1 1
## 25 0.470452877 3 3
## 26 0.595001631 3 3
## 27 0.443799113 3 3
## 28 0.512785470 3 3
## 29 0.054528030 1 1removed_pop <- c("Fonte_tassete_Prati_di_Tivo","Rascafria","Sainte_Baume","Saro_Vasterskog","Serra_di_Fiumorbu")
Genomic_offset_combined_randomAF_elev <- merge(Genomic_offset_combined_random_AF,meta_data_pop_order[,c(2,6)],"Population")
Genomic_offset_combined_randomAF_elev$Elevation.DEM_90m.<- as.numeric(Genomic_offset_combined_randomAF_elev$Elevation.DEM_90m.)
Genomic_offset_combined_randomAF_elev$Latitude<- as.numeric(Genomic_offset_combined_randomAF_elev$Latitude)
Genomic_offset_combined_randomAF_elev_reduce <- Genomic_offset_combined_randomAF_elev[-c(9,19,21,22,23),]
cor(Genomic_offset_combined_randomAF_elev_reduce$GO_GF,Genomic_offset_combined_randomAF_elev_reduce$Elevation.DEM_90m.)## [1] 0.7217921cor(Genomic_offset_combined_randomAF_elev_reduce$GO_RDA,Genomic_offset_combined_randomAF_elev_reduce$Elevation.DEM_90m.)## [1] 0.6029465Genomic_offset_combined_randomAF_elev_reduce <- Genomic_offset_combined_randomAF_elev_reduce %>%
mutate(mean_values = (GO_GF + GO_RDA) / 2)
cor(Genomic_offset_combined_randomAF_elev_reduce$mean_values,Genomic_offset_combined_randomAF_elev_reduce$Elevation.DEM_90m.)## [1] 0.6499934cor(Genomic_offset_combined_randomAF_elev_reduce$mean_values,Genomic_offset_combined_randomAF_elev_reduce$Latitude)## [1] -0.7102025s <- cor.test(Genomic_offset_combined_randomAF_elev_reduce$mean_values,Genomic_offset_combined_randomAF_elev_reduce$Elevation.DEM_90m.)
s$p.value## [1] 0.0005860178s <- cor.test(Genomic_offset_combined_randomAF_elev_reduce$mean_values,Genomic_offset_combined_randomAF_elev_reduce$Latitude)
s$p.value## [1] 0.0001010691Genomic_offset_combined_randomAF_elev_reduce$pop_abbrev <- c("ANC","BLI","BOM","BRA","BUJA","BUJG","CAR","CED","UMB","GOR","HAR","HHR","JUR","CHO","OLY","VOU","PAT","RWM","SUE","UNS","VAL","VIS","WAL","YEW")
#plot
plot_elevation <- ggplot(data=Genomic_offset_combined_randomAF_elev_reduce, aes(x=Elevation.DEM_90m.,y=mean_values))+
geom_point(aes(size=Latitude)) +
geom_text(label=Genomic_offset_combined_randomAF_elev_reduce$pop_abbrev,vjust=1.8)
pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/Elevation_Latitude_gradients/plot_GO_elevation.pdf");print(plot_elevation);dev.off()## png
## 2 plot_elevation
plot_latitude <- ggplot(data=Genomic_offset_combined_randomAF_elev_reduce, aes(x=Latitude,y=mean_values))+
geom_point()+
geom_text(label=Genomic_offset_combined_randomAF_elev_reduce$pop_abbrev,vjust=1.3)
pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/Elevation_Latitude_gradients/plot_GO_latitude.pdf");print(plot_latitude);dev.off()## png
## 2 plot_latitude
#cor GO per method
cor(Genomic_offset_combined_randomAF_elev_reduce$GO_RDA,Genomic_offset_combined_randomAF_elev_reduce$Latitude)## [1] -0.6617696cor(Genomic_offset_combined_randomAF_elev_reduce$GO_GF,Genomic_offset_combined_randomAF_elev_reduce$Latitude)## [1] -0.7809332#all outliers
Genomic_offset_combined_alloutliers_elev <-merge(Genomic_offset_combined_all_outliers,meta_data_pop_order[,c(2,6)],"Population")
Genomic_offset_combined_alloutliers_elev$Elevation.DEM_90m.<- as.numeric(Genomic_offset_combined_alloutliers_elev$Elevation.DEM_90m.)
Genomic_offset_combined_allout_elev_reduce <- Genomic_offset_combined_alloutliers_elev[-c(3,21),]
cor(Genomic_offset_combined_allout_elev_reduce$GO_GF,Genomic_offset_combined_allout_elev_reduce$Elevation.DEM_90m.)## [1] 0.6010643cor(Genomic_offset_combined_allout_elev_reduce$GO_RDA,Genomic_offset_combined_allout_elev_reduce$Elevation.DEM_90m.)## [1] 0.5529983cor(Genomic_offset_combined_allout_elev_reduce$GO_GF,Genomic_offset_combined_allout_elev_reduce$Latitude)## [1] -0.5503734cor(Genomic_offset_combined_allout_elev_reduce$GO_RDA,Genomic_offset_combined_allout_elev_reduce$Latitude)## [1] -0.5013869GOOD FIGURE
Combined random AF
removed_pop <- c("Fonte_tassete_Prati_di_Tivo","Rascafria","Sainte_Baume","Saro_Vasterskog","Serra_di_Fiumorbu")
shapefile <- st_read("C:/Users/tfrancisco/Documents/Thesis/Data/Species/Taxus_baccata/shapefiles/Taxus_baccata_plg_clip.shp")## Reading layer `Taxus_baccata_plg_clip' from data source
## `C:\Users\tfrancisco\Documents\Thesis\Data\Species\Taxus_baccata\shapefiles\Taxus_baccata_plg_clip.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 55 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -10.11699 ymin: 32.58137 xmax: 55.22354 ymax: 62.89065
## Geodetic CRS: WGS 84library(colorBlindness)
colors <- c("darkgreen","lightgreen","#FDF7B3",
"#FC4E2A","red")
colors <- c("#004CFF","#00E5FF","#FFFF80","#FFAA00","#FF0000")
colors <- c("darkgreen","lightgreen","#FFFF80","#FFAA00","#FF0000")
colors <- c("#290AD8","#AAF7FF","#FFFFBF","#F76D5E","#FF0000")
colors <- c("#005000","#85B22C","#FFFFBF","#F76D5E","#E51932")
#colors <- c("#005000","#85B22C","#ffff6d","#F76D5E","#E51932")
#standardised
Genomic_offset_combined_random_AF$scaled_GO_RDA <- (Genomic_offset_combined_random_AF$GO_RDA-min(Genomic_offset_combined_random_AF$GO_RDA))/(max(Genomic_offset_combined_random_AF$GO_RDA)-min(Genomic_offset_combined_random_AF$GO_RDA))
Genomic_offset_combined_random_AF$scaled_GO_GF <- (Genomic_offset_combined_random_AF$GO_GF-min(Genomic_offset_combined_random_AF$GO_GF))/(max(Genomic_offset_combined_random_AF$GO_GF)-min(Genomic_offset_combined_random_AF$GO_GF))
plot <- ggplot(data = Genomic_offset_combined_random_AF) +
geom_sf(data = admin, fill = gray(0.92), size = 0) +
geom_sf(data = admin, fill = NA, size = 0.1) +
geom_sf(data = shapefile, fill = "lightgrey", color = "black", size = 0.2) + # Adding shapefile
coord_sf(xlim = c(-10, 30), ylim = c(35, 62), expand = FALSE) +
# Plot half-circles as usual for the genomic offsets
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = -pi/2, end = pi/2, fill = GO_GF_interval
), color = "black", data = Genomic_offset_combined_random_AF) +
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = pi/2, end = 3 * pi/2, fill = GO_RDA_interval
), color = "black", data = Genomic_offset_combined_random_AF) +
# Add asterisk (*) slightly to the left of the circle for low-trust populations
geom_text(
data = Genomic_offset_combined_random_AF %>% filter(Population %in% removed_pop),
aes(x = Longitude + 0.75, y = Latitude+0.3, label = "*"), # Adjust the offset value as needed
color = "red", size = 7.5, fontface = "bold"
) +
# Color scale and other plot details
scale_fill_manual(
name = NULL, # Removes the legend title
values = colors,
labels = c("0", "", "0.5", "", "1"),
drop = FALSE, na.translate = FALSE
) +
annotate("text", x = Inf, y = Inf, label = "* Low-trust predictions",
hjust = 1.2, vjust = 2, color = "red", size = 3.5, fontface = "bold") +
xlab("Longitude") + ylab("Latitude") +
ggtitle("Genomic offset across populations") +
annotation_scale(location = "br", width_hint = 0.3) + # Scale bar at bottom left
annotation_north_arrow(location = "tl", which_north = "true",
pad_x = unit(-0.2, "cm"), pad_y = unit(0.08, "cm"),
style = north_arrow_fancy_orienteering) + # Compass rose
theme_bw(base_size = 11) +
theme(
legend.position = "right", panel.grid = element_blank(),
strip.text = element_text(size = 11),
plot.title = element_text(hjust = 0.5, color = "Black", face = "italic")
)
print(plot)
pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/Final_figures/GO_map_final_highli_bad_predict_new_colorsV2.pdf");print(plot);dev.off()## png
## 2removed_pop <- c("Fonte_tassete_Prati_di_Tivo","Rascafria","Sainte_Baume","Saro_Vasterskog","Serra_di_Fiumorbu")
shapefile <- st_read("C:/Users/tfrancisco/Documents/Thesis/Data/Species/Taxus_baccata/shapefiles/Taxus_baccata_plg_clip.shp")## Reading layer `Taxus_baccata_plg_clip' from data source
## `C:\Users\tfrancisco\Documents\Thesis\Data\Species\Taxus_baccata\shapefiles\Taxus_baccata_plg_clip.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 55 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -10.11699 ymin: 32.58137 xmax: 55.22354 ymax: 62.89065
## Geodetic CRS: WGS 84 colors <- c("darkgreen", "#FDF7B3", "#FC4E2A", "#BD0026", "darkorchid4")
colors <- c("#005000","#85B22C","#FFFFBF","#F76D5E","#E51932")
plot <- ggplot(data = Genomic_offset_combined_random_AF) +
geom_sf(data = admin, fill = gray(0.92), size = 0) +
geom_sf(data = admin, fill = NA, size = 0.1) +
geom_sf(data = shapefile, fill = "lightgrey", color = "black", size = 0.2) + # Adding shapefile
coord_sf(xlim = c(-10, 30), ylim = c(35, 62), expand = FALSE) +
# Plot half-circles as usual for the genomic offsets
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = -pi/2, end = pi/2, fill = scaled_GO_GF # Ensure it's numeric
), color = "black", data = Genomic_offset_combined_random_AF) +
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = pi/2, end = 3 * pi/2, fill = scaled_GO_RDA # Ensure it's numeric
), color = "black", data = Genomic_offset_combined_random_AF) +
# Add asterisk (*) slightly to the left of the circle for low-trust populations
geom_text(
data = Genomic_offset_combined_random_AF %>% filter(Population %in% removed_pop),
aes(x = Longitude + 0.75, y = Latitude + 0.3, label = "*"), # Adjust the offset value as needed
color = "red", size = 7.5, fontface = "bold"
) +
# Color gradient scale with more pronounced red for higher values
scale_fill_gradient(
name = NULL, # Removes the legend title
low = "yellow", high = "red",
limits = c(0, 1), # Explicitly set the range to enhance contrast
labels = c("0", "", "0.5", "", "1"),
na.value = "gray", guide = guide_colorbar(title = "Genomic Offset")
) +
annotate("text", x = Inf, y = Inf, label = "* Low-trust predictions",
hjust = 1.2, vjust = 2, color = "red", size = 3.5, fontface = "bold") +
xlab("Longitude") + ylab("Latitude") +
ggtitle("Genomic offset across populations") +
annotation_scale(location = "br", width_hint = 0.3) + # Scale bar at bottom left
annotation_north_arrow(location = "tl", which_north = "true",
pad_x = unit(-0.2, "cm"), pad_y = unit(0.08, "cm"),
style = north_arrow_fancy_orienteering) + # Compass rose
theme_bw(base_size = 11) +
theme(
legend.position = "right", panel.grid = element_blank(),
strip.text = element_text(size = 11),
plot.title = element_text(hjust = 0.5, color = "Black", face = "italic")
)
print(plot)
#pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/Final_figures/GO_map_final_highli_bad_predict.pdf");print(plot);dev.off()Combined random AF V2
removed_pop <- c("Fonte_tassete_Prati_di_Tivo","Rascafria","Sainte_Baume")
shapefile <- st_read("C:/Users/tfrancisco/Documents/Thesis/Data/Species/Taxus_baccata/shapefiles/Taxus_baccata_plg_clip.shp")## Reading layer `Taxus_baccata_plg_clip' from data source
## `C:\Users\tfrancisco\Documents\Thesis\Data\Species\Taxus_baccata\shapefiles\Taxus_baccata_plg_clip.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 55 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -10.11699 ymin: 32.58137 xmax: 55.22354 ymax: 62.89065
## Geodetic CRS: WGS 84colors <- c("#005000","#85B22C","#FFFFBF","#F76D5E","#E51932")
colors <- c("#005000","#85B22C","#FFFFBF","#F76D5E","#E51932")
Genomic_offset_combined_random_AF2$scaled_GO_RDA <- (Genomic_offset_combined_random_AF2$GO_RDA-min(Genomic_offset_combined_random_AF2$GO_RDA))/(max(Genomic_offset_combined_random_AF2$GO_RDA)-min(Genomic_offset_combined_random_AF2$GO_RDA))
Genomic_offset_combined_random_AF2$scaled_GO_GF <- (Genomic_offset_combined_random_AF2$GO_GF-min(Genomic_offset_combined_random_AF2$GO_GF))/(max(Genomic_offset_combined_random_AF2$GO_GF)-min(Genomic_offset_combined_random_AF2$GO_GF))
plot <- ggplot(data = Genomic_offset_combined_random_AF2) +
geom_sf(data = admin, fill = gray(0.92), size = 0) +
geom_sf(data = admin, fill = NA, size = 0.1) +
geom_sf(data = shapefile, fill = "lightgrey", color = "black", size = 0.2) + # Adding shapefile
coord_sf(xlim = c(-10, 30), ylim = c(35, 62), expand = FALSE) +
# Plot half-circles as usual for the genomic offsets
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = -pi/2, end = pi/2, fill = GO_GF_interval
), color = "black", data = Genomic_offset_combined_random_AF2) +
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = pi/2, end = 3 * pi/2, fill = GO_RDA_interval
), color = "black", data = Genomic_offset_combined_random_AF2) +
# Add asterisk (*) slightly to the left of the circle for low-trust populations
geom_text(
data = Genomic_offset_combined_random_AF2 %>% filter(Population %in% removed_pop),
aes(x = Longitude + 0.75, y = Latitude+0.3, label = "*"), # Adjust the offset value as needed
color = "red", size = 7.5, fontface = "bold"
) +
# Color scale and other plot details
scale_fill_manual(
name = NULL, # Removes the legend title
values = colors,
labels = c("0", "", "0.5", "", "1"),
drop = FALSE, na.translate = FALSE
) +
annotate("text", x = Inf, y = Inf, label = "* Low-trust predictions",
hjust = 1.2, vjust = 2, color = "red", size = 3.5, fontface = "bold") +
xlab("Longitude") + ylab("Latitude") +
ggtitle("Genomic offset across populations") +
annotation_scale(location = "br", width_hint = 0.3) + # Scale bar at bottom left
annotation_north_arrow(location = "tl", which_north = "true",
pad_x = unit(-0.2, "cm"), pad_y = unit(0.08, "cm"),
style = north_arrow_fancy_orienteering) + # Compass rose
theme_bw(base_size = 11) +
theme(
legend.position = "right", panel.grid = element_blank(),
strip.text = element_text(size = 11),
plot.title = element_text(hjust = 0.5, color = "Black", face = "italic")
)
print(plot)
pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/Final_figures/GO_map_final_highli_bad_predict_randomV2_new_colors.pdf");print(plot);dev.off()## png
## 2Combined outliers
removed_pop <- c("Bohinj_Obrne_Mokri_Log","Bujaruelo-ADAPCON","Bujaruelo-GENTREE-1","Cardo","Jura","Mt_Vourinos","Sainte_Baume","Unska_kolisevka","Valditacca","Wallis")
shapefile <- st_read("C:/Users/tfrancisco/Documents/Thesis/Data/Species/Taxus_baccata/shapefiles/Taxus_baccata_plg_clip.shp")## Reading layer `Taxus_baccata_plg_clip' from data source
## `C:\Users\tfrancisco\Documents\Thesis\Data\Species\Taxus_baccata\shapefiles\Taxus_baccata_plg_clip.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 55 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -10.11699 ymin: 32.58137 xmax: 55.22354 ymax: 62.89065
## Geodetic CRS: WGS 84colors <- c("#005000","#85B22C","#FFFFBF","#F76D5E","#E51932")
Genomic_offset_combined_outlier$scaled_GO_RDA <- (Genomic_offset_combined_outlier$GO_RDA-min(Genomic_offset_combined_outlier$GO_RDA))/(max(Genomic_offset_combined_outlier$GO_RDA)-min(Genomic_offset_combined_outlier$GO_RDA))
Genomic_offset_combined_outlier$scaled_GO_GF <- (Genomic_offset_combined_outlier$GO_GF-min(Genomic_offset_combined_outlier$GO_GF))/(max(Genomic_offset_combined_outlier$GO_GF)-min(Genomic_offset_combined_outlier$GO_GF))
plot <- ggplot(data = Genomic_offset_combined_outlier) +
geom_sf(data = admin, fill = gray(0.92), size = 0) +
geom_sf(data = admin, fill = NA, size = 0.1) +
geom_sf(data = shapefile, fill = "lightgrey", color = "black", size = 0.2) + # Adding shapefile
coord_sf(xlim = c(-10, 30), ylim = c(35, 62), expand = FALSE) +
# Plot half-circles as usual for the genomic offsets
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = -pi/2, end = pi/2, fill = GO_GF_interval
), color = "black", data = Genomic_offset_combined_outlier) +
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = pi/2, end = 3 * pi/2, fill = GO_RDA_interval
), color = "black", data = Genomic_offset_combined_outlier) +
# Add asterisk (*) slightly to the left of the circle for low-trust populations
geom_text(
data = Genomic_offset_combined_outlier %>% filter(Population %in% removed_pop),
aes(x = Longitude + 0.75, y = Latitude+0.3, label = "*"), # Adjust the offset value as needed
color = "red", size = 7.5, fontface = "bold"
) +
# Color scale and other plot details
scale_fill_manual(
name = NULL, # Removes the legend title
values = colors,
labels = c("0", "", "0.5", "", "1"),
drop = FALSE, na.translate = FALSE
) +
annotate("text", x = Inf, y = Inf, label = "* Low-trust predictions",
hjust = 1.2, vjust = 2, color = "red", size = 3.5, fontface = "bold") +
xlab("Longitude") + ylab("Latitude") +
ggtitle("Genomic offset across populations") +
annotation_scale(location = "br", width_hint = 0.3) + # Scale bar at bottom left
annotation_north_arrow(location = "tl", which_north = "true",
pad_x = unit(-0.2, "cm"), pad_y = unit(0.08, "cm"),
style = north_arrow_fancy_orienteering) + # Compass rose
theme_bw(base_size = 11) +
theme(
legend.position = "right", panel.grid = element_blank(),
strip.text = element_text(size = 11),
plot.title = element_text(hjust = 0.5, color = "Black", face = "italic")
)
print(plot)
pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/Final_figures/GO_map_final_highli_bad_predict_outliers_newcolors.pdf");print(plot);dev.off()## png
## 2Combined all outliers
removed_pop <- c("Bohinj_Obrne_Mokri_Log","Sainte_Baume")
shapefile <- st_read("C:/Users/tfrancisco/Documents/Thesis/Data/Species/Taxus_baccata/shapefiles/Taxus_baccata_plg_clip.shp")## Reading layer `Taxus_baccata_plg_clip' from data source
## `C:\Users\tfrancisco\Documents\Thesis\Data\Species\Taxus_baccata\shapefiles\Taxus_baccata_plg_clip.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 55 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -10.11699 ymin: 32.58137 xmax: 55.22354 ymax: 62.89065
## Geodetic CRS: WGS 84colors <- c("#005000","#85B22C","#FFFFBF","#F76D5E","#E51932")
Genomic_offset_combined_all_outliers$scaled_GO_RDA <- (Genomic_offset_combined_all_outliers$GO_RDA-min(Genomic_offset_combined_all_outliers$GO_RDA))/(max(Genomic_offset_combined_all_outliers$GO_RDA)-min(Genomic_offset_combined_all_outliers$GO_RDA))
Genomic_offset_combined_all_outliers$scaled_GO_GF <- (Genomic_offset_combined_all_outliers$GO_GF-min(Genomic_offset_combined_all_outliers$GO_GF))/(max(Genomic_offset_combined_all_outliers$GO_GF)-min(Genomic_offset_combined_all_outliers$GO_GF))
plot <- ggplot(data = Genomic_offset_combined_all_outliers) +
geom_sf(data = admin, fill = gray(0.92), size = 0) +
geom_sf(data = admin, fill = NA, size = 0.1) +
geom_sf(data = shapefile, fill = "lightgrey", color = "black", size = 0.2) + # Adding shapefile
coord_sf(xlim = c(-10, 30), ylim = c(35, 62), expand = FALSE) +
# Plot half-circles as usual for the genomic offsets
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = -pi/2, end = pi/2, fill = GO_GF_interval
), color = "black", data = Genomic_offset_combined_all_outliers) +
geom_arc_bar(aes(
x0 = Longitude, y0 = Latitude, r0 = 0, r = 0.5,
start = pi/2, end = 3 * pi/2, fill = GO_RDA_interval
), color = "black", data = Genomic_offset_combined_all_outliers) +
# Add asterisk (*) slightly to the left of the circle for low-trust populations
geom_text(
data = Genomic_offset_combined_all_outliers %>% filter(Population %in% removed_pop),
aes(x = Longitude + 0.75, y = Latitude+0.3, label = "*"), # Adjust the offset value as needed
color = "red", size = 7.5, fontface = "bold"
) +
# Color scale and other plot details
scale_fill_manual(
name = NULL, # Removes the legend title
values = colors,
labels = c("0", "", "0.5", "", "1"),
drop = FALSE, na.translate = FALSE
) +
annotate("text", x = Inf, y = Inf, label = "* Low-trust predictions",
hjust = 1.2, vjust = 2, color = "red", size = 3.5, fontface = "bold") +
xlab("Longitude") + ylab("Latitude") +
ggtitle("Genomic offset across populations") +
annotation_scale(location = "br", width_hint = 0.3) + # Scale bar at bottom left
annotation_north_arrow(location = "tl", which_north = "true",
pad_x = unit(-0.2, "cm"), pad_y = unit(0.08, "cm"),
style = north_arrow_fancy_orienteering) + # Compass rose
theme_bw(base_size = 11) +
theme(
legend.position = "right", panel.grid = element_blank(),
strip.text = element_text(size = 11),
plot.title = element_text(hjust = 0.5, color = "Black", face = "italic")
)
print(plot)
pdf("C:/Users/tfrancisco/Documents/Thesis/Results/species/taxus/Genomic_offset/Final_figures/GO_map_final_highli_bad_predict_alloutliers_newcolors.pdf");print(plot);dev.off()## png
## 2DRAFT: Points are more transparent
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.3.2 (2023-10-31 ucrt)
## os Windows 11 x64 (build 26100)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate French_France.utf8
## ctype French_France.utf8
## tz Europe/Paris
## date 2025-09-22
## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## bslib 0.6.1 2023-11-28 [1] CRAN (R 4.3.2)
## cachem 1.0.8 2023-05-01 [1] CRAN (R 4.3.1)
## class 7.3-22 2023-05-03 [2] CRAN (R 4.3.2)
## classInt 0.4-10 2023-09-05 [1] CRAN (R 4.3.2)
## cli 3.6.1 2023-03-23 [1] CRAN (R 4.3.1)
## codetools 0.2-19 2023-02-01 [2] CRAN (R 4.3.2)
## colorBlindness * 0.1.9 2021-04-17 [1] CRAN (R 4.3.3)
## colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.3.1)
## corrplot * 0.92 2021-11-18 [1] CRAN (R 4.3.2)
## cowplot 1.1.3 2024-01-22 [1] CRAN (R 4.3.2)
## DBI 1.2.2 2024-02-16 [1] CRAN (R 4.3.2)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.3.3)
## digest 0.6.33 2023-07-07 [1] CRAN (R 4.3.1)
## dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.3.2)
## e1071 1.7-14 2023-12-06 [1] CRAN (R 4.3.2)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.3.1)
## evaluate 0.23 2023-11-01 [1] CRAN (R 4.3.2)
## farver 2.1.2 2024-05-13 [1] CRAN (R 4.3.3)
## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.1)
## fs 1.6.3 2023-07-20 [1] CRAN (R 4.3.1)
## generics 0.1.4 2025-05-09 [1] CRAN (R 4.3.2)
## ggforce * 0.4.1 2022-10-04 [1] CRAN (R 4.3.2)
## ggplot2 * 3.5.2 2025-04-09 [1] CRAN (R 4.3.2)
## ggspatial * 1.1.9 2023-08-17 [1] CRAN (R 4.3.3)
## glue 1.7.0 2024-01-09 [1] CRAN (R 4.3.2)
## gridGraphics 0.5-1 2020-12-13 [1] CRAN (R 4.3.2)
## gtable 0.3.6 2024-10-25 [1] CRAN (R 4.3.3)
## highr 0.10 2022-12-22 [1] CRAN (R 4.3.1)
## htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.3.2)
## htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.3.2)
## httpuv 1.6.13 2023-12-06 [1] CRAN (R 4.3.2)
## httr 1.4.7 2023-08-15 [1] CRAN (R 4.3.2)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.3.1)
## jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.3.2)
## KernSmooth 2.23-22 2023-07-10 [2] CRAN (R 4.3.2)
## knitr 1.45 2023-10-30 [1] CRAN (R 4.3.2)
## labeling 0.4.3 2023-08-29 [1] CRAN (R 4.3.1)
## later 1.3.2 2023-12-06 [1] CRAN (R 4.3.2)
## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.2)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.1)
## MASS 7.3-60.0.1 2024-01-13 [2] CRAN (R 4.3.2)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.3.1)
## mime 0.12 2021-09-28 [1] CRAN (R 4.3.1)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.3.2)
## munsell 0.5.1 2024-04-01 [1] CRAN (R 4.3.3)
## pillar 1.10.2 2025-04-05 [1] CRAN (R 4.3.3)
## pkgbuild 1.4.3 2023-12-10 [1] CRAN (R 4.3.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.2)
## pkgload 1.3.4 2024-01-16 [1] CRAN (R 4.3.2)
## polyclip 1.10-6 2023-09-27 [1] CRAN (R 4.3.1)
## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.3.2)
## promises 1.2.1 2023-08-10 [1] CRAN (R 4.3.2)
## proxy 0.4-27 2022-06-09 [1] CRAN (R 4.3.2)
## purrr 1.0.2 2023-08-10 [1] CRAN (R 4.3.2)
## R6 2.6.1 2025-02-15 [1] CRAN (R 4.3.3)
## RColorBrewer * 1.1-3 2022-04-03 [1] CRAN (R 4.3.1)
## Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.3.1)
## remotes 2.5.0 2024-03-17 [1] CRAN (R 4.3.3)
## rlang 1.1.3 2024-01-10 [1] CRAN (R 4.3.2)
## rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.3.1)
## rnaturalearth * 1.0.1 2023-12-15 [1] CRAN (R 4.3.2)
## rnaturalearthdata 1.0.0 2024-02-09 [1] CRAN (R 4.3.2)
## rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.3.2)
## sass 0.4.8 2023-12-06 [1] CRAN (R 4.3.2)
## scales 1.3.0 2023-11-28 [1] CRAN (R 4.3.2)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.2)
## sf * 1.0-15 2023-12-18 [1] CRAN (R 4.3.2)
## shiny 1.8.0 2023-11-17 [1] CRAN (R 4.3.2)
## stringi 1.8.3 2023-12-11 [1] CRAN (R 4.3.2)
## stringr 1.5.1 2023-11-14 [1] CRAN (R 4.3.2)
## terra 1.7-71 2024-01-31 [1] CRAN (R 4.3.2)
## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.2)
## tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.3.3)
## tweenr 2.0.2 2022-09-06 [1] CRAN (R 4.3.2)
## units 0.8-5 2023-11-28 [1] CRAN (R 4.3.2)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.3.2)
## usethis 2.2.3 2024-02-19 [1] CRAN (R 4.3.2)
## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.2)
## withr 3.0.2 2024-10-28 [1] CRAN (R 4.3.3)
## xfun 0.41 2023-11-01 [1] CRAN (R 4.3.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.3.2)
## yaml 2.3.8 2023-12-11 [1] CRAN (R 4.3.2)
##
## [1] C:/Users/tfrancisco/AppData/Local/R/win-library/4.3
## [2] C:/Users/tfrancisco/AppData/Local/Programs/R/R-4.3.2/library
##
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