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PCA_comparison.R
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PCA_comparison.R
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library(tidyverse)
library(readxl)
library(adegenet)
library(poppr)
library(radiator)
library(RColorBrewer)
library(ggrepel)
library(BiocManager)
#BiocManager::install("YuLab-SMU/treedataverse")
library(treedataverse)
#### Read in Amplicon genepop file ####
Amp_gen <- read.genepop("X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/CE_vs_Amp/Amplicon_genepop.gen",
ncode = 3L,
quiet = FALSE)
# Read in CE vs Amp population data
Amp_pop_data <- read_excel("X:/2201_BKT_msat_conversion/samplesForCEComparison.xlsx") %>%
filter(SampleID %in% rownames(Amp_gen@tab)) %>%
select(SampleID, Location) %>%
rename(WaterbodyName = Location) %>%
arrange(match(SampleID, rownames(Amp_gen@tab)))
# Fill pop slot
Amp_gen@pop <- as_factor(Amp_pop_data$WaterbodyName)
#### Read in data ####
Data_2111 <- read.genepop("X:/2111_F1F2D_BKT/2111analysis/Thometz_scripts/2111_genepop.gen",
ncode = 3L,
quiet = FALSE)
# Read in 2111 metadata
Samples_2111 <- read_delim("X:/2111_F1F2D_BKT/2111analysis/Thometz_scripts/Samples_2111.csv") %>%
arrange(Cohort) %>%
filter(SampleID %in% rownames(Data_2111@tab)) %>%
arrange(match(SampleID, rownames(Data_2111@tab))) %>%
mutate(WaterbodyName = case_when(str_detect(WaterbodyName, "St. Croix") ~ "Hatchery",
.default = WaterbodyName))
# Fill 2111 pop slot and subset to just domestic fish
Data_2111@pop <- as_factor(Samples_2111$WaterbodyName)
Domestics <- popsub(Data_2111,
sublist = "Hatchery",
drop = FALSE)
# Filter to CE vs Amp loci
Domestics <- Domestics[loc = locNames(Amp_gen)]
# Read in 2205 genetic data for reference comparisons
Data_2205 <- read.genepop("X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/2205_genepop.gen",
ncode = 3L,
quiet = FALSE)
# # Filter to CE vs Amp loci
# temp_gen <- Data_2205[loc = locNames(Amp_gen)]
#
# # Write and read it back in to work around repooling error
# temp_gen %>%
# tidy_genind() %>%
# write_genepop(genepop.header = "59 Survey pops with just 7 CE vs Amp loci",
# filename = "X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/Erdman_integration/2205_pops_7_loci")
#Data_2205_7_loci <- read.genepop("X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/CE_vs_Amp/2205_pops_7_loci.gen",
# ncode = 3L,
# quiet = FALSE)
# Read in project metadata
Samples_2205 <- read_delim("X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/Samples_2205.csv") %>%
filter(SampleID %in% rownames(Data_2205_7_loci@tab)) %>%
arrange(match(SampleID, rownames(Data_2205_7_loci@tab))) %>%
select(WaterbodyName, SampleID, WBIC, HUC_8, HUC_6, HUC_4, HUC_2)
Data_2205_7_loci@pop <- as_factor(Samples_2205$WaterbodyName)
# Repool
All_dat_amplicon <- repool(Data_2205_7_loci, Domestics, Amp_gen)
# Create df with all SampleID's and Waterbodies
centroid_df_amp <- Samples_2111 %>%
bind_rows(Samples_2111) %>%
bind_rows(Amp_pop_data) %>%
select(SampleID, WaterbodyName)
#### Run PCA with amplicon data ####
pca_amp_data <- tab(All_dat_amplicon, freq = TRUE, NA.method = "mean")
pca_amp_result <- dudi.pca(pca_amp_data, center = TRUE, scale = FALSE, nf = 4, scannf = FALSE)
pop_names_1 <- All_dat_amplicon@pop %>%
as_tibble() %>%
rename(pop = value)
pca_df_1 <- pca_amp_result$li %>%
as_tibble() %>%
mutate(Population = pop_names_1$pop)
# Plot
centroids_pop_1 <- pca_df_1 %>%
select(Population) %>%
right_join(aggregate(cbind(Axis1, Axis2) ~ Population, pca_df_1, mean)) %>%
distinct(.keep_all = TRUE)
pca_amp_plot <- pca_df_1 %>%
ggplot(aes(x = Axis1, y = Axis2)) +
#geom_point(alpha = 0.4) +
#stat_ellipse() +
geom_point(data = centroids_pop_1,
size = 6,
alpha = 0.8) +
geom_text_repel(data = centroids_pop_1,
aes(label = Population),
fontface = "bold",
size = 5, force = 5,
force_pull = 0.1,
max.overlaps = 100) +
ylab("Axis 2") +
xlab("Axis 1") +
theme_classic(base_size = 18) +
theme(legend.position = "none")
ggsave(filename = "PCA_amplicon.pdf",
plot = pca_amp_plot,
device = "pdf",
path = "X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/CE_vs_Amp/Plots",
height = 12,
width = 12,
units = "in")
######################## Do this again with converted CE data ##################################
#### Read in converted CE genepop file ####
CE_converted <- read.genepop("X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/CE_vs_Amp/CE_genepop_converted.gen",
ncode = 3L,
quiet = FALSE)
# Read in CE population data
CE_pop_data <- read_excel("X:/2201_BKT_msat_conversion/samplesForCEComparison.xlsx") %>%
filter(SampleID %in% rownames(CE_converted@tab)) %>%
select(SampleID, Location) %>%
rename(WaterbodyName = Location) %>%
arrange(match(SampleID, rownames(CE_converted@tab)))
# Fill 2111 pop slot and subset to just domestic fish
CE_converted@pop <- as_factor(CE_pop_data$WaterbodyName)
# Repool
All_dat_converted <- repool(Data_2205_7_loci, Domestics, CE_converted)
# Create df with all SampleID's and Waterbodies
centroid_df_CE <- Samples_2111 %>%
bind_rows(Samples_2111) %>%
bind_rows(CE_pop_data) %>%
select(SampleID, WaterbodyName)
#### Run PCA with amplicon data ####
pca_CE_data <- tab(All_dat_converted, freq = TRUE, NA.method = "mean")
pca_CE_result <- dudi.pca(pca_CE_data, center = TRUE, scale = FALSE, nf = 4, scannf = FALSE)
pop_names_2 <- All_dat_converted@pop %>%
as_tibble() %>%
rename(pop = value)
pca_df_2 <- pca_CE_result$li %>%
as_tibble() %>%
mutate(Population = pop_names_2$pop)
# Plot
centroids_pop_2 <- pca_df_2 %>%
select(Population) %>%
right_join(aggregate(cbind(Axis1, Axis2) ~ Population, pca_df_2, mean)) %>%
distinct(.keep_all = TRUE)
pca_CE_plot <- pca_df_2 %>%
ggplot(aes(x = Axis1, y = Axis2)) +
#geom_point(alpha = 0.4) +
#stat_ellipse() +
geom_point(data = centroids_pop_2,
size = 6,
alpha = 0.8) +
geom_text_repel(data = centroids_pop_2,
aes(label = Population),
fontface = "bold",
size = 5, force = 5,
force_pull = 0.1,
max.overlaps = 100) +
ylab("Axis 2") +
xlab("Axis 1") +
theme_classic(base_size = 18) +
theme(legend.position = "none")
ggsave(filename = "PCA_converted_CE.pdf",
plot = pca_CE_plot,
device = "pdf",
path = "X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/CE_vs_Amp/Plots",
height = 12,
width = 12,
units = "in")