Script for Fig. 3b, 3d and Table S4 qPCR Transplant qPCR results

```{r}
#### percent change in D. trenchii transplanted offshore ####
# Make faceted boxplots per host species showing change in relative abundances of D. trenchii during reciprocal transplantation inshore/offshore Palau.

library(ggplot2)
library(dplyr)
library(tidyr)
library(readr)
library(rstatix)

in_to_off <- read.csv('in_to_off_CT.csv', header = TRUE)
off_to_in <- read.csv('off_to_in_CT.csv', header = TRUE)

# calculate cell number from CT values based on standard curve
process_data <- function(data, C_CT_col, D_CT_col) {
  data %>%
    mutate(
      C_cell = (10^((!!sym(C_CT_col) - 20.947) / -3.6424)) * 4000000,  # Cladocopium
      D_cell = (10^((!!sym(D_CT_col) - 19.278) / -3.6406)) * 4000000,  # Durusdinium
      C_cell = ifelse(!!sym(C_CT_col) < 31, C_cell, 0),
      D_cell = ifelse(!!sym(D_CT_col) < 31, D_cell, 0),
      prop_d = D_cell / (D_cell + C_cell),
      prop_d = ifelse(is.nan(prop_d), NA, prop_d),
      prop_d = case_when(
        C_cell == 0 ~ 1,  # If no Cladocopium, 100% Durusdinium
        D_cell == 0 ~ 0,  # If no Durusdinium, 0% Durusdinium
        TRUE ~ prop_d
      ),
      perc_d = prop_d * 100  # Convert proportion to percentage
    )
}

# Load and process both transplant datasets
in_to_off <- read.csv('in_to_off_CT.csv', header = TRUE) %>%
  process_data("C_CT", "D_CT")
off_to_in <- read.csv('off_to_in_CT.csv', header = TRUE) %>%
  process_data("Cladocopium_CT", "Dtrenchii_CT")

# Combine the datasets by adding a new column to distinguish between them
combined_data <- bind_rows(
  off_to_in %>% mutate(Transplant = "Offshore to Inshore"), 
  in_to_off %>% mutate(Transplant = "Inshore to Offshore")
)

# Save the combined dataset to a CSV file
write_csv(combined_data, "Table_S4_Transplant_qPCR_data.csv")

# Reorder the 'Timepoint' factor in combined_data
combined_data$Timepoint <- factor(combined_data$Timepoint, levels = c("T0", "6M", "1Y", "2Y"))

# Visualization for in_to_off
ggplot(in_to_off, aes(x = Timepoint, y = perc_d, fill = Host_genus)) + 
  geom_boxplot() + 
  geom_jitter(position = position_jitter(width = 0.2, height = 0), alpha = 0.5) +
  facet_wrap(~Host_genus) + 
  labs(y = bquote('Relative abundance of D. trenchii'), x = "Time") +
  theme_pubr(base_size = 20) +
  border("black") +
  ylim(0, 120) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Visualization for off_to_in
ggplot(off_to_in, aes(x = Timepoint, y = perc_d, fill = Host_genus)) + 
  geom_boxplot() + 
  geom_jitter(position = position_jitter(width = 0.2, height = 0), alpha = 0.5) +
  facet_wrap(~Host_genus) + 
  labs(y = bquote('Relative abundance of D. trenchii'), x = "Time") +
  theme_pubr(base_size = 20) +
  border("black") +
  ylim(0, 120) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))