Resazurin Assays - USDA M.gigas Families in Response to 36C and Freshwater Co-stressors

2026
Pacific oyster
resazurin
freshwater stress
heat stress
Magallana gigas
Crassostrea gigas
Author

Sam White

Published

May 5, 2026

INTRO

This post presents results from resazurin assays conducted on families of Magallana gigas (Pacific oyster) in response to freshwater stress at 36°C over the course of 4hrs.

METHODS

Resazurin assays

Oysters from each of the nine families were distributed across nine, 12-well plates and submerged in 4mL of resazurin working solution prepared with TAPWATER. Each plate included one well with working solution only as a negative control. Plates were held at 36°C for 4hrs, and fluorescence was measured periodically using a Synergy HTX (Agilent) plate reader.

Oyster measurements

Oyster area was measured using ImageJ. Oysters were photographed in their plates with a ruler for scale, and the area of each oyster was calculated using ImageJ “Measure Particles” tool.

Data analysis

Analysis was conducted in this R Markdown file:

RESULTS

Resazurin assays were conducted on nine USDA M. gigas families (1, 3, 5, 6, 7, 8, 9, 9b, 10; n=11/family) under combined freshwater and heat (36°C) stress over 4 hours. All nine plates passed consistency checks (12 wells per plate per timepoint), and no individual wells were excluded from analysis.

Linear mixed-effects model analysis of size-normalized metabolism over time showed highly significant effects of time (F=202.77, p<0.0001), family (F=4.23, p=0.0002), and a significant time × family interaction (F=1.50, p=0.0445), indicating that families diverged in their metabolic trajectories as the co-stressor exposure progressed.

AUC analysis confirmed a significant family effect on overall metabolic output (F=5.22, p<0.0001). Family 9 showed the highest mean AUC (0.041 ± 0.004 SE), while Family 7 had the lowest (0.018 ± 0.001 SE). Post-hoc Tukey comparisons identified the following significant pairwise differences in AUC:

  • Family 9 > Family 7 (p<0.0001)
  • Family 9 > Family 3 (p=0.002)
  • Family 9b > Family 7 (p=0.003)
  • Family 5 > Family 7 (p=0.007)
  • Family 6 > Family 7 (p=0.010)

These results suggest that families 9, 9b, 5, and 6 maintained comparatively higher metabolic activity under combined freshwater and thermal stress, while Family 7 consistently exhibited the lowest metabolic response.

Important

AI Use Level 4: Substantial AI contribution

The rendered markdown is below.

1 Background

M. gigas oysters from nine USDA families were placed individually in clear 12-well plates and submerged in 4 mL of resazurin working solution prepared with tap water to assess combined freshwater and temperature stress responses. Plates were incubated at 36°C for the duration of the experiment. At each designated timepoint, plates were transferred to a Synergy HTX (Agilent) plate reader and fluorescence was measured directly in the 12-well plates using the Gen5 software (Agilent).

See Resazurin/data/20260505-mgig-freshwater-36C/README.md for full experimental notes.

1.1 Expected inputs

Path Description
Resazurin/data/20260505-mgig-freshwater-36C/plate-*-T*.txt Plate reader fluorescence exports (one file per plate per timepoint)
Resazurin/data/20260505-mgig-freshwater-36C/layout.csv Well metadata: plate ID, well ID, blank flag, family groups, sample IDs, area measurements (mm², from ImageJ)

1.2 Expected outputs

All outputs are written to Resazurin/outputs/01.00-resazurin-20260505-mgig-freshwater-36C/.

File Description
figures/ All plots generated by this script
auc_all_metrics.csv Per-individual AUC values for every active measurement metric
auc_summary.csv Group-level AUC summary statistics (mean, SD, SE, median)
metabolism.csv Full per-well per-timepoint metabolism data frame
pairwise_stats.csv Tukey-adjusted pairwise comparisons from AUC linear models

2 Setup

2.1 Knitr options

knitr::opts_chunk$set(
  echo = TRUE,         # Display code chunks
  eval = TRUE,        # Evaluate code chunks
  warning = FALSE,     # Hide warnings
  message = FALSE,     # Hide messages
  comment = "",         # Prevents appending '##' to beginning of lines in code output
  results = 'hold'     # Holds output so it's all printed together after code chunk
)

2.2 Load libraries

library(tidyverse)
library(pracma)       # trapz()
library(lme4)
library(lmerTest)
library(emmeans)
library(multcompView)
library(cowplot)
library(colorspace)   # qualitative_hcl() for large palettes

3 Helper Functions

normalize_well_id <- function(x) {
  x <- toupper(trimws(x))
  valid <- str_detect(x, "^[A-Z]+[0-9]+$")
  out <- rep(NA_character_, length(x))
  if (!any(valid)) return(out)
  m <- str_match(x[valid], "^([A-Z]+)([0-9]+)$")
  out[valid] <- paste0(m[, 2], as.integer(m[, 3]))
  out
}

parse_time_hr <- function(path) {
  hit <- str_match(basename(path),
                   "(?i)-T([0-9]+(?:\\.[0-9]+)?)\\.txt$")
  as.numeric(hit[, 2])
}

parse_plate_id <- function(path) {
  hit <- str_match(basename(path),
    "(?i)^plate-([A-Za-z0-9-]+)-T[0-9]+(?:\\.[0-9]+)?\\.txt$")
  id <- hit[, 2]
  ifelse(is.na(id), "unknown", id)
}

extract_results_block <- function(lines) {
  results_idx <- which(trimws(lines) == "Results")
  if (length(results_idx) == 0) stop("No Results section found")
  idx <- results_idx[1]
  header_tokens <- str_split(lines[idx + 1], "\\t")[[1]] |> trimws()
  col_ids <- header_tokens[
    header_tokens != "" & str_detect(header_tokens, "^[0-9]+$")]
  j <- idx + 2
  data_lines <- character()
  while (j <= length(lines)) {
    line <- lines[j]
    if (trimws(line) == "") break
    if (!str_detect(line, "^[A-Za-z]\\t")) break
    data_lines <- c(data_lines, line)
    j <- j + 1
  }
  list(col_ids = col_ids, data_lines = data_lines)
}

parse_plate_export <- function(path) {
  lines <- readLines(path, warn = FALSE)
  res <- extract_results_block(lines)

  map_dfr(res$data_lines, function(line) {
    tokens <- str_split(line, "\\t")[[1]] |> trimws()
    tokens <- tokens[tokens != ""]
    row_letter <- tokens[1]
    nums <- suppressWarnings(as.numeric(tokens[-1]))
    valid_idx <- which(!is.na(nums))
    if (length(valid_idx) == 0) return(tibble())
    vals <- nums[valid_idx]
    n <- min(length(vals), length(res$col_ids))
    tibble(
      row_id  = toupper(row_letter),
      col_id  = as.integer(res$col_ids[seq_len(n)]),
      well_id = normalize_well_id(
        paste0(toupper(row_letter), res$col_ids[seq_len(n)])),
      value   = vals[seq_len(n)]
    )
  }) %>%
    mutate(
      plate_id = str_to_lower(parse_plate_id(path)),
      time_hr  = parse_time_hr(path)
    )
}

trapezoid_auc <- function(time_hr, value) {
  ok <- is.finite(time_hr) & is.finite(value)
  t <- time_hr[ok]
  v <- value[ok]
  if (length(t) < 2) return(NA_real_)
  ord <- order(t)
  t <- t[ord]; v <- v[ord]
  sum(diff(t) * (head(v, -1) + tail(v, -1)) / 2)
}

# Shared helper: extract display unit string from a measurement column name.
# e.g. "area_mm2_measurement" -> "mm²", "weight_mg_measurement" -> "mg"
parse_meas_unit <- function(col_name) {
  unit_raw <- col_name |>
    str_remove("^metabolism_per_") |>
    str_remove("_measurement$") |>
    str_extract("[^_]+$")
  case_when(
    unit_raw == "mm2" ~ "mm²",
    unit_raw == "cm2" ~ "cm²",
    unit_raw == "mm3" ~ "mm³",
    unit_raw == "cm3" ~ "cm³",
    TRUE              ~ unit_raw
  )
}

# y-axis label for metabolism line plots: "fold change/mm²"
metabolism_y_label <- function(col_name) {
  paste0("Metabolism (fold change/", parse_meas_unit(col_name), ")")
}

# y-axis label for AUC box plots: "Metabolism (AUC; mm²)"
auc_y_label <- function(metric_name) {
  paste0("Metabolism (AUC; ", parse_meas_unit(metric_name), ")")
}

4 Load Data

4.1 Plate export files

proj_root <- rprojroot::find_rstudio_root_file()
data_dir  <- file.path(proj_root, "Resazurin", "data", "20260505-mgig-freshwater-36C")
out_dir   <- file.path(proj_root, "Resazurin", "outputs",
                        "01.00-resazurin-20260505-mgig-freshwater-36C")
fig_dir   <- file.path(out_dir, "figures")

dir.create(fig_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)

plate_files <- list.files(
  data_dir,
  pattern = "(?i)^plate-.*-T[0-9]+(?:\\.[0-9]+)?\\.txt$",
  full.names = TRUE
)

plate_raw <- map_dfr(plate_files, function(path) {
  tryCatch(parse_plate_export(path),
           error = function(e) {
             message("Parse error in ", basename(path), ": ", e$message)
             tibble()
           })
})

str(plate_raw)
tibble [540 × 6] (S3: tbl_df/tbl/data.frame)
 $ row_id  : chr [1:540] "A" "A" "A" "A" ...
 $ col_id  : int [1:540] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id : chr [1:540] "A1" "A2" "A3" "A4" ...
 $ value   : num [1:540] 111 109 116 107 104 120 103 104 106 124 ...
 $ plate_id: chr [1:540] "b" "b" "b" "b" ...
 $ time_hr : num [1:540] 0 0 0 0 0 0 0 0 0 0 ...

4.2 Plate consistency check

Checks that every plate has the same number of wells at every timepoint. The expected well count is the mode across all plate × timepoint reads. Any plate with at least one deviating read is flagged and dropped entirely before any further analysis — removing only the aberrant timepoint would break the fold-change baseline calculation.

well_counts <- plate_raw %>%
  group_by(plate_id, time_hr) %>%
  summarise(n_wells = n_distinct(well_id), .groups = "drop")

expected_n_wells <- as.integer(
  names(which.max(table(well_counts$n_wells)))
)

inconsistent_reads <- well_counts %>%
  filter(n_wells != expected_n_wells) %>%
  arrange(plate_id, time_hr)

inconsistent_plate_ids <- unique(inconsistent_reads$plate_id)

if (nrow(inconsistent_reads) > 0) {
  cat("**Plate consistency check FAILED.**",
      "Expected", expected_n_wells, "wells per plate-timepoint read.",
      length(inconsistent_plate_ids),
      "plate(s) have at least one deviating read and are excluded",
      "from all analyses:\n\n")
  cat(knitr::kable(
    inconsistent_reads,
    col.names = c("Plate", "Time (h)", "Wells read"),
    caption   = paste("Expected:", expected_n_wells, "wells per read")
  ), sep = "\n")
  cat("\n")
  plate_raw <- plate_raw %>%
    filter(!plate_id %in% inconsistent_plate_ids)
  message(length(inconsistent_plate_ids),
          " plate(s) removed from plate_raw: ",
          paste(inconsistent_plate_ids, collapse = ", "))
} else {
  cat("Plate consistency check passed: all",
      n_distinct(well_counts$plate_id), "plates have",
      expected_n_wells, "wells at every timepoint.\n")
}

Plate consistency check passed: all 9 plates have 12 wells at every timepoint.

4.3 Layout file

layout_path <- file.path(data_dir, "layout.csv")

layout_raw <- read_csv(layout_path,
                       col_types = cols(.default = "c"),
                       show_col_types = FALSE)

# Standardise column names to snake_case
names(layout_raw) <- names(layout_raw) |>
  str_to_lower() |>
  str_replace_all("[^a-z0-9]+", "_") |>
  str_replace_all("_+", "_") |>
  str_replace("_$", "")

# Normalise plate_id to match plate file ids (strip "plate-" prefix)
layout_clean <- layout_raw %>%
  mutate(
    plate_id = str_remove(str_to_lower(plate_id), "^plate-"),
    well_id  = normalize_well_id(plate_well),
    is_blank = if ("is_blank" %in% names(layout_raw))
      toupper(trimws(is_blank)) %in% c("TRUE", "T", "1", "YES", "Y")
    else
      FALSE
  )

found_exclude_col <- intersect(
  c("exclude_from_analysis", "exclude", "omit", "not_analyzed"),
  names(layout_clean)
)[1]
layout_clean <- layout_clean %>%
  mutate(
    exclude_from_analysis = if (!is.na(found_exclude_col))
      toupper(trimws(.data[[found_exclude_col]])) %in%
        c("TRUE", "T", "1", "YES", "Y")
    else
      FALSE
  )

# Identify measurement columns and group columns
measurement_cols <- names(layout_clean)[
  str_detect(names(layout_clean), "_measurement$")]
group_cols <- names(layout_clean)[
  str_detect(names(layout_clean), "_group$")]

# Cast measurement columns to numeric
layout_clean <- layout_clean %>%
  mutate(across(all_of(measurement_cols),
                ~ suppressWarnings(as.numeric(.x))))

# Determine which measurement columns actually contain finite data
active_meas_cols <- measurement_cols[
  sapply(measurement_cols, function(col)
    any(is.finite(layout_clean[[col]]), na.rm = TRUE))]

# Normalise group values to lowercase so they match colour scale definitions
layout_clean <- layout_clean %>%
  mutate(across(all_of(group_cols),
                ~ str_to_lower(trimws(as.character(.x)))))

message("Group columns: ", paste(group_cols, collapse = ", "))
message("Active measurement columns: ",
        paste(active_meas_cols, collapse = ", "))

str(layout_clean)
tibble [108 × 13] (S3: tbl_df/tbl/data.frame)
 $ plate_id             : chr [1:108] "b" "b" "b" "b" ...
 $ plate_well           : chr [1:108] "A01" "A02" "A03" "A04" ...
 $ is_blank             : logi [1:108] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:108] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:108] "1" "2" "3" "4" ...
 $ weight_g_measurement : num [1:108] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:108] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:108] NA NA NA NA NA NA NA NA NA NA ...
 $ treatment_group      : chr [1:108] NA NA NA NA ...
 $ area_mm2_measurement : num [1:108] 123.3 91.2 161.7 105.8 75.1 ...
 $ imagej_id            : chr [1:108] "1" "2" "3" "4" ...
 $ well_id              : chr [1:108] "A1" "A2" "A3" "A4" ...
 $ exclude_from_analysis: logi [1:108] FALSE FALSE FALSE FALSE FALSE FALSE ...

5 Merge Plate Data with Layout

dat <- plate_raw %>%
  left_join(
    layout_clean %>%
      select(plate_id, well_id, is_blank, exclude_from_analysis,
             any_of("exclude_reason"),
             all_of(group_cols), all_of(measurement_cols)),
    by = c("plate_id", "well_id")
  ) %>%
  mutate(
    is_blank = replace_na(is_blank, FALSE),
    exclude_from_analysis = replace_na(exclude_from_analysis, FALSE)
  )

str(dat)
tibble [540 × 15] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:540] "A" "A" "A" "A" ...
 $ col_id               : int [1:540] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:540] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:540] 111 109 116 107 104 120 103 104 106 124 ...
 $ plate_id             : chr [1:540] "b" "b" "b" "b" ...
 $ time_hr              : num [1:540] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:540] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:540] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:540] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:540] "1" "2" "3" "4" ...
 $ treatment_group      : chr [1:540] NA NA NA NA ...
 $ weight_g_measurement : num [1:540] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:540] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:540] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement : num [1:540] 123.3 91.2 161.7 105.8 75.1 ...

6 Raw Fluorescence

6.1 Data frame

# Wells in the plate reader output that have no layout entry get all-NA group
# columns after the join. Keep only wells assigned to at least one group.
active_gc <- intersect(group_cols, names(dat))

raw_df <- dat %>%
  filter(
    !is_blank,
    if (length(active_gc) > 0)
      if_any(all_of(active_gc), ~ !is.na(.))
    else
      TRUE
  ) %>%
  mutate(
    trace_id = if_else(
      !is.na(sample_id_group) & trimws(as.character(sample_id_group)) != "",
      as.character(sample_id_group),
      paste(plate_id, well_id, sep = "_")
    )
  )

families   <- sort(unique(na.omit(raw_df$family_id_group)))
treatments <- sort(unique(na.omit(raw_df$treatment_group)))

n_fam <- length(families)
n_trt <- length(treatments)

# Palette strategy:
#   <= 7 groups : Okabe-Ito (gold standard for colorblind-safe figures).
#   >  7 groups : colorspace::qualitative_hcl("Dynamic") scales to any N
#                 using perceptually uniform HCL space — no colour collisions.
# Black (#000000) is excluded from both and reserved for blank wells.
okabe_ito_7 <- c(
  "#E69F00", "#56B4E9", "#009E73", "#F0E442",
  "#0072B2", "#D55E00", "#CC79A7"
)
make_palette <- function(n) {
  if (n == 0L) return(character(0))
  if (n <= length(okabe_ito_7)) return(okabe_ito_7[seq_len(n)])
  colorspace::qualitative_hcl(n, palette = "Dynamic")
}

all_colours   <- make_palette(n_fam + n_trt)
fam_colours   <- setNames(all_colours[seq_len(n_fam)], families)
trt_colours   <- setNames(all_colours[n_fam + seq_len(n_trt)], treatments)

lty_pool <- c("solid", "dashed", "dotted", "dotdash", "longdash")
trt_linetypes <- setNames(
  lty_pool[(seq_len(n_trt) - 1L) %% length(lty_pool) + 1L],
  treatments
)
plate_well_colours <- c(blank = "black", fam_colours)

has_trt <- n_trt > 0

str(raw_df)
tibble [495 × 16] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:495] "A" "A" "A" "A" ...
 $ col_id               : int [1:495] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:495] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:495] 111 109 116 107 104 120 103 104 106 124 ...
 $ plate_id             : chr [1:495] "b" "b" "b" "b" ...
 $ time_hr              : num [1:495] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:495] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:495] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:495] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:495] "1" "2" "3" "4" ...
 $ treatment_group      : chr [1:495] NA NA NA NA ...
 $ weight_g_measurement : num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement : num [1:495] 123.3 91.2 161.7 105.8 75.1 ...
 $ trace_id             : chr [1:495] "1" "2" "3" "4" ...

6.2 Raw fluorescence by plate (including blanks)

p_raw_plates <- dat %>%
  filter(is.finite(time_hr), is.finite(value)) %>%
  mutate(
    colour_group = if_else(is_blank, "blank",
                           coalesce(family_id_group, "sample")),
    trace_id     = paste(plate_id, well_id, sep = "_")
  ) %>%
  ggplot(aes(x = time_hr, y = value,
             group = trace_id, colour = colour_group)) +
  geom_line(alpha = 0.6) +
  geom_point(size = 1, alpha = 0.7) +
  facet_wrap(~ plate_id) +
  scale_colour_manual(
    values   = plate_well_colours,
    name     = "Group",
    breaks   = names(plate_well_colours),
    na.value = "grey80"
  ) +
  labs(x = "Time (h)", y = "Raw fluorescence (RFU)") +
  theme_classic(base_size = 12) +
  theme(strip.background = element_blank(),
        strip.text       = element_text(face = "bold"))

p_raw_plates

ggsave(file.path(fig_dir, "raw_fluor_by_plate.png"),
       p_raw_plates, width = 10, height = 8)

6.3 Mean raw fluorescence by family

raw_family_summary <- raw_df %>%
  filter(!is.na(family_id_group), !exclude_from_analysis) %>%
  group_by(family_id_group, treatment_group, time_hr) %>%
  summarise(
    mean_fluor = mean(value, na.rm = TRUE),
    se_fluor   = sd(value, na.rm = TRUE) /
      sqrt(sum(!is.na(value))),
    n          = sum(!is.na(value)),
    .groups    = "drop"
  ) %>%
  mutate(group_var = if (has_trt)
    paste(family_id_group, treatment_group, sep = ".")
  else
    family_id_group)

p_raw_mean <- ggplot(raw_family_summary,
    aes(x = time_hr, y = mean_fluor,
        colour = family_id_group,
        group = group_var)) +
  geom_ribbon(aes(ymin = mean_fluor - se_fluor,
                  ymax = mean_fluor + se_fluor,
                  fill = family_id_group),
              alpha = 0.15, colour = NA) +
  geom_line(
    mapping   = if (has_trt) aes(linetype = treatment_group) else NULL,
    linewidth = 1) +
  geom_point(size = 2) +
  scale_colour_manual(values = fam_colours, name = "Family") +
  scale_fill_manual(values = fam_colours, name = "Family") +
  labs(x = "Time (h)", y = "Mean raw fluorescence (RFU ± SE)") +
  theme_classic(base_size = 13) +
  if (has_trt) scale_linetype_manual(values = trt_linetypes, name = "Treatment") else NULL

p_raw_mean

ggsave(file.path(fig_dir, "raw_mean_by_family.png"),
       p_raw_mean, width = 8, height = 5)

6.4 Individual raw fluorescence traces by family

p_raw_by_family <- raw_df %>%
  filter(!is.na(family_id_group)) %>%
  ggplot(aes(x = time_hr, y = value, group = trace_id,
             colour = .data[[if (has_trt) "treatment_group" else "family_id_group"]])) +
  geom_line(alpha = 0.6) +
  geom_point(size = 1.2, alpha = 0.7) +
  facet_wrap(~ family_id_group) +
  scale_colour_manual(
    values = if (has_trt) trt_colours else fam_colours,
    name   = if (has_trt) "Treatment" else "Family") +
  labs(x = "Time (h)", y = "Raw fluorescence (RFU)") +
  theme_classic(base_size = 12) +
  theme(strip.background = element_blank(),
        strip.text       = element_text(face = "bold"))

p_raw_by_family

ggsave(file.path(fig_dir, "raw_individual_by_family.png"),
       p_raw_by_family, width = 10, height = 5)

6.5 Individual raw fluorescence traces by treatment

if (has_trt) {
  p_raw_by_treatment <- raw_df %>%
    ggplot(aes(x = time_hr, y = value,
               group = trace_id, colour = family_id_group)) +
    geom_line(alpha = 0.6) +
    geom_point(size = 1.2, alpha = 0.7) +
    facet_wrap(~ treatment_group) +
    scale_colour_manual(values = fam_colours, name = "Family") +
    labs(x = "Time (h)", y = "Raw fluorescence (RFU)") +
    theme_classic(base_size = 12) +
    theme(strip.background = element_blank(),
          strip.text       = element_text(face = "bold"))

  p_raw_by_treatment
  ggsave(file.path(fig_dir, "raw_individual_by_treatment.png"),
         p_raw_by_treatment, width = 10, height = 5)
}

6.6 Excluded samples

Wells flagged exclude_from_analysis = TRUE appear in the raw fluorescence plots above but are omitted from all analyses that follow.

excluded_wells <- dat %>%
  filter(!is_blank, exclude_from_analysis) %>%
  mutate(
    sample = if_else(
      !is.na(sample_id_group) & trimws(as.character(sample_id_group)) != "",
      as.character(sample_id_group),
      paste(plate_id, well_id, sep = "_")
    )
  ) %>%
  select(plate_id, well_id, sample, family_id_group, treatment_group,
         any_of("exclude_reason")) %>%
  distinct() %>%
  arrange(plate_id, well_id)

if (nrow(excluded_wells) > 0) {
  col_names <- c("Plate", "Well", "Sample", "Family", "Treatment")
  if ("exclude_reason" %in% names(excluded_wells))
    col_names <- c(col_names, "Reason")
  cat(knitr::kable(excluded_wells, col.names = col_names), sep = "\n")
} else {
  cat("No wells are excluded from analysis.\n")
}

No wells are excluded from analysis.

7 Blank Correction via Fold-Change Normalization

T0 is the earliest timepoint present in the dataset (not necessarily 0 hr). Sample fold-change is expressed relative to each individual’s T0 reading, resolved by sample_id_group when that column is populated — allowing the same animal to be tracked across plates — or by plate_id + well_id when no sample IDs exist (backward-compatible with single-plate, multi-timepoint designs). Blank fold-change is the per-plate mean blank RFU at each timepoint divided by the pooled mean blank RFU at T0. Subtracting blank fold-change from sample fold-change removes background fluorescence drift; all samples start at exactly 0 at T0 by construction.

7.1 Step 1 – Identify T0 and compute per-sample fold-change

# T0 = earliest timepoint present in the dataset
t0_time <- min(dat$time_hr[is.finite(dat$time_hr)], na.rm = TRUE)
message("T0 timepoint: ", t0_time, " hr")

# T0 reference value per individual.
# Resolved by sample_id_group (cross-plate tracking) when available;
# falls back to plate+well for layouts without explicit sample IDs.
t0_all <- dat %>%
  filter(time_hr == t0_time, !is_blank, is.finite(value)) %>%
  mutate(sample_key = if_else(
    !is.na(sample_id_group) & trimws(as.character(sample_id_group)) != "",
    as.character(sample_id_group),
    paste(plate_id, well_id, sep = "_")
  )) %>%
  group_by(sample_key) %>%
  summarise(value_t0 = mean(value, na.rm = TRUE), .groups = "drop")

dat_fc <- dat %>%
  mutate(sample_key = if_else(
    !is_blank &
      !is.na(sample_id_group) & trimws(as.character(sample_id_group)) != "",
    as.character(sample_id_group),
    paste(plate_id, well_id, sep = "_")
  )) %>%
  left_join(t0_all, by = "sample_key") %>%
  mutate(fold_change = if_else(
    !is_blank & is.finite(value_t0) & value_t0 > 0,
    value / value_t0,
    NA_real_
  ))

str(dat_fc)
tibble [540 × 18] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:540] "A" "A" "A" "A" ...
 $ col_id               : int [1:540] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:540] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:540] 111 109 116 107 104 120 103 104 106 124 ...
 $ plate_id             : chr [1:540] "b" "b" "b" "b" ...
 $ time_hr              : num [1:540] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:540] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:540] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:540] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:540] "1" "2" "3" "4" ...
 $ treatment_group      : chr [1:540] NA NA NA NA ...
 $ weight_g_measurement : num [1:540] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:540] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:540] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement : num [1:540] 123.3 91.2 161.7 105.8 75.1 ...
 $ sample_key           : chr [1:540] "1" "2" "3" "4" ...
 $ value_t0             : num [1:540] 111 109 116 107 104 120 103 104 106 124 ...
 $ fold_change          : num [1:540] 1 1 1 1 1 1 1 1 1 1 ...

7.2 Step 2 – Blank fold-change reference per plate per timepoint

# Pooled mean blank RFU at T0 across all T0 plates
mean_blank_t0 <- dat %>%
  filter(is_blank, time_hr == t0_time, is.finite(value)) %>%
  pull(value) %>%
  mean(na.rm = TRUE)

if (!is.finite(mean_blank_t0))
  message("No blank readings found at T0 (", t0_time,
          " hr); blank correction will produce NA.")

# Per-plate per-timepoint mean blank expressed as fold-change relative to T0
blank_fc_ref <- dat %>%
  filter(is_blank, is.finite(value)) %>%
  group_by(plate_id, time_hr) %>%
  summarise(mean_blank_rfu = mean(value, na.rm = TRUE), .groups = "drop") %>%
  mutate(mean_blank_fc = mean_blank_rfu / mean_blank_t0)

str(blank_fc_ref)
tibble [45 × 4] (S3: tbl_df/tbl/data.frame)
 $ plate_id      : chr [1:45] "b" "b" "b" "b" ...
 $ time_hr       : num [1:45] 0 1 2 3 4 0 1 2 3 4 ...
 $ mean_blank_rfu: num [1:45] 99 103 111 115 119 97 102 110 113 118 ...
 $ mean_blank_fc : num [1:45] 0.987 1.027 1.106 1.146 1.186 ...

7.3 Step 3 – Subtract blank fold-change from sample fold-change

samples <- dat_fc %>%
  filter(!is_blank, !exclude_from_analysis) %>%
  mutate(
    trace_id = if_else(
      !is.na(sample_id_group) & trimws(as.character(sample_id_group)) != "",
      as.character(sample_id_group),
      paste(plate_id, well_id, sep = "_")
    )
  ) %>%
  left_join(blank_fc_ref, by = c("plate_id", "time_hr")) %>%
  mutate(corrected_fc = fold_change - mean_blank_fc)

str(samples)
tibble [495 × 22] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:495] "A" "A" "A" "A" ...
 $ col_id               : int [1:495] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:495] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:495] 111 109 116 107 104 120 103 104 106 124 ...
 $ plate_id             : chr [1:495] "b" "b" "b" "b" ...
 $ time_hr              : num [1:495] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:495] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:495] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:495] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:495] "1" "2" "3" "4" ...
 $ treatment_group      : chr [1:495] NA NA NA NA ...
 $ weight_g_measurement : num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement : num [1:495] 123.3 91.2 161.7 105.8 75.1 ...
 $ sample_key           : chr [1:495] "1" "2" "3" "4" ...
 $ value_t0             : num [1:495] 111 109 116 107 104 120 103 104 106 124 ...
 $ fold_change          : num [1:495] 1 1 1 1 1 1 1 1 1 1 ...
 $ trace_id             : chr [1:495] "1" "2" "3" "4" ...
 $ mean_blank_rfu       : num [1:495] 99 99 99 99 99 99 99 99 99 99 ...
 $ mean_blank_fc        : num [1:495] 0.987 0.987 0.987 0.987 0.987 ...
 $ corrected_fc         : num [1:495] 0.0133 0.0133 0.0133 0.0133 0.0133 ...

8 Blank-Corrected Fold-Change

8.1 Mean by family

bc_fc_summary <- samples %>%
  filter(!is.na(family_id_group), !exclude_from_analysis) %>%
  group_by(family_id_group, treatment_group, time_hr) %>%
  summarise(
    mean_val = mean(corrected_fc, na.rm = TRUE),
    se_val   = sd(corrected_fc, na.rm = TRUE) /
      sqrt(sum(!is.na(corrected_fc))),
    n        = sum(!is.na(corrected_fc)),
    .groups  = "drop"
  ) %>%
  mutate(group_var = if (has_trt)
    paste(family_id_group, treatment_group, sep = ".")
  else
    family_id_group)

p_bc_fc_mean <- ggplot(bc_fc_summary,
    aes(x = time_hr, y = mean_val,
        colour = family_id_group,
        group = group_var)) +
  geom_ribbon(aes(ymin = mean_val - se_val,
                  ymax = mean_val + se_val,
                  fill = family_id_group),
              alpha = 0.15, colour = NA) +
  geom_line(
    mapping   = if (has_trt) aes(linetype = treatment_group) else NULL,
    linewidth = 1) +
  geom_point(size = 2) +
  scale_colour_manual(values = fam_colours, name = "Family") +
  scale_fill_manual(values = fam_colours, name = "Family") +
  labs(x = "Time (h)",
       y = "Mean blank-corrected fold-change (± SE)") +
  theme_classic(base_size = 13) +
  if (has_trt) scale_linetype_manual(values = trt_linetypes, name = "Treatment") else NULL

p_bc_fc_mean

ggsave(file.path(fig_dir, "blank_corrected_fc_mean_by_family.png"),
       p_bc_fc_mean, width = 8, height = 5)

8.2 Individual traces by family

p_bc_fc_by_family <- samples %>%
  filter(!is.na(family_id_group)) %>%
  ggplot(aes(x = time_hr, y = corrected_fc, group = trace_id,
             colour = .data[[if (has_trt) "treatment_group" else "family_id_group"]])) +
  geom_line(alpha = 0.6) +
  geom_point(size = 1.2, alpha = 0.7) +
  facet_wrap(~ family_id_group) +
  scale_colour_manual(
    values = if (has_trt) trt_colours else fam_colours,
    name   = if (has_trt) "Treatment" else "Family") +
  labs(x = "Time (h)", y = "Blank-corrected fold-change") +
  theme_classic(base_size = 12) +
  theme(strip.background = element_blank(),
        strip.text       = element_text(face = "bold"))

p_bc_fc_by_family

ggsave(file.path(fig_dir, "blank_corrected_fc_by_family.png"),
       p_bc_fc_by_family, width = 10, height = 5)

8.3 Individual blank-corrected fold-change traces by treatment

if (has_trt) {
  p_bc_fc_by_treatment <- samples %>%
    ggplot(aes(x = time_hr, y = corrected_fc,
               group = trace_id, colour = family_id_group)) +
    geom_line(alpha = 0.6) +
    geom_point(size = 1.2, alpha = 0.7) +
    facet_wrap(~ treatment_group) +
    scale_colour_manual(values = fam_colours, name = "Family") +
    labs(x = "Time (h)", y = "Blank-corrected fold-change") +
    theme_classic(base_size = 12) +
    theme(strip.background = element_blank(),
          strip.text       = element_text(face = "bold"))

  p_bc_fc_by_treatment
  ggsave(file.path(fig_dir, "blank_corrected_fc_by_treatment.png"),
         p_bc_fc_by_treatment, width = 10, height = 5)
}

9 Metabolism (Size-Normalised Fold-Change)

Blank-corrected fold-change divided by each active measurement column. This is “metabolism” as defined in Huffmyer et al.

if (length(active_meas_cols) == 0) {
  message("No active measurement columns: skipping metabolism calculation.")
  metabolism_df <- tibble()
} else {
  metabolism_df <- samples
  for (mc in active_meas_cols) {
    out_col <- paste0("metabolism_per_", mc)
    metabolism_df <- metabolism_df %>%
      mutate(!!out_col := if_else(
        is.finite(.data[[mc]]) & .data[[mc]] > 0 &
          is.finite(corrected_fc),
        corrected_fc / .data[[mc]],
        NA_real_
      ))
  }
}

str(metabolism_df)
tibble [495 × 23] (S3: tbl_df/tbl/data.frame)
 $ row_id                             : chr [1:495] "A" "A" "A" "A" ...
 $ col_id                             : int [1:495] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id                            : chr [1:495] "A1" "A2" "A3" "A4" ...
 $ value                              : num [1:495] 111 109 116 107 104 120 103 104 106 124 ...
 $ plate_id                           : chr [1:495] "b" "b" "b" "b" ...
 $ time_hr                            : num [1:495] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank                           : logi [1:495] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis              : logi [1:495] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group                    : chr [1:495] "6" "6" "6" "6" ...
 $ sample_id_group                    : chr [1:495] "1" "2" "3" "4" ...
 $ treatment_group                    : chr [1:495] NA NA NA NA ...
 $ weight_g_measurement               : num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement               : num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement              : num [1:495] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement               : num [1:495] 123.3 91.2 161.7 105.8 75.1 ...
 $ sample_key                         : chr [1:495] "1" "2" "3" "4" ...
 $ value_t0                           : num [1:495] 111 109 116 107 104 120 103 104 106 124 ...
 $ fold_change                        : num [1:495] 1 1 1 1 1 1 1 1 1 1 ...
 $ trace_id                           : chr [1:495] "1" "2" "3" "4" ...
 $ mean_blank_rfu                     : num [1:495] 99 99 99 99 99 99 99 99 99 99 ...
 $ mean_blank_fc                      : num [1:495] 0.987 0.987 0.987 0.987 0.987 ...
 $ corrected_fc                       : num [1:495] 0.0133 0.0133 0.0133 0.0133 0.0133 ...
 $ metabolism_per_area_mm2_measurement: num [1:495] 1.08e-04 1.46e-04 8.22e-05 1.26e-04 1.77e-04 ...

9.1 Mean metabolism by family

if (nrow(metabolism_df) > 0) {

  metab_cols <- paste0("metabolism_per_", active_meas_cols)

  for (col in metab_cols) {
    if (!col %in% names(metabolism_df)) next
    mc_label <- str_remove(col, "^metabolism_per_")

    metab_summary <- metabolism_df %>%
      filter(!is.na(family_id_group), !exclude_from_analysis) %>%
      group_by(family_id_group, treatment_group, time_hr) %>%
      summarise(
        mean_val = mean(.data[[col]], na.rm = TRUE),
        se_val   = sd(.data[[col]], na.rm = TRUE) /
          sqrt(sum(!is.na(.data[[col]]))),
        .groups  = "drop"
      ) %>%
      mutate(group_var = if (has_trt)
        paste(family_id_group, treatment_group, sep = ".")
      else
        family_id_group)

    p_metab_mean <- ggplot(metab_summary,
        aes(x = time_hr, y = mean_val,
            colour = family_id_group,
            group = group_var)) +
      geom_ribbon(aes(ymin = mean_val - se_val,
                      ymax = mean_val + se_val,
                      fill = family_id_group),
                  alpha = 0.15, colour = NA) +
      geom_line(
        mapping   = if (has_trt) aes(linetype = treatment_group) else NULL,
        linewidth = 1) +
      geom_point(size = 2) +
      scale_colour_manual(values = fam_colours, name = "Family") +
      scale_fill_manual(values = fam_colours, name = "Family") +
      labs(x = "Time (h)",
           y = paste0(metabolism_y_label(col), " (± SE)")) +
      theme_classic(base_size = 13) +
      if (has_trt) scale_linetype_manual(values = trt_linetypes, name = "Treatment") else NULL

    print(p_metab_mean)
    ggsave(
      file.path(fig_dir,
                paste0("metabolism_mean_", mc_label, ".png")),
      p_metab_mean, width = 8, height = 5)
  }
}

9.2 Individual metabolism traces by family

if (nrow(metabolism_df) > 0) {

  for (col in metab_cols) {
    if (!col %in% names(metabolism_df)) next
    mc_label <- str_remove(col, "^metabolism_per_")

    p_metab_by_family <- metabolism_df %>%
      filter(!is.na(family_id_group)) %>%
      ggplot(aes(x = time_hr, y = .data[[col]], group = trace_id,
                 colour = .data[[if (has_trt) "treatment_group" else "family_id_group"]])) +
      geom_line(alpha = 0.6) +
      geom_point(size = 1.2, alpha = 0.7) +
      facet_wrap(~ family_id_group) +
      scale_colour_manual(
        values = if (has_trt) trt_colours else fam_colours,
        name   = if (has_trt) "Treatment" else "Family") +
      labs(x = "Time (h)", y = metabolism_y_label(col)) +
      theme_classic(base_size = 12) +
      theme(strip.background = element_blank(),
            strip.text       = element_text(face = "bold"))

    print(p_metab_by_family)
    ggsave(
      file.path(fig_dir,
                paste0("metabolism_individual_", mc_label, "_by_family.png")),
      p_metab_by_family, width = 10, height = 5)

    if (has_trt) {
      p_metab_by_treatment <- ggplot(metabolism_df,
          aes(x = time_hr, y = .data[[col]],
              group = trace_id, colour = family_id_group)) +
        geom_line(alpha = 0.6) +
        geom_point(size = 1.2, alpha = 0.7) +
        facet_wrap(~ treatment_group) +
        scale_colour_manual(values = fam_colours, name = "Family") +
        labs(x = "Time (h)", y = metabolism_y_label(col)) +
        theme_classic(base_size = 12) +
        theme(strip.background = element_blank(),
              strip.text       = element_text(face = "bold"))

      print(p_metab_by_treatment)
      ggsave(
        file.path(fig_dir,
                  paste0("metabolism_individual_", mc_label, "_by_treatment.png")),
        p_metab_by_treatment, width = 10, height = 5)
    }
  }
}

10 Time-Series Statistical Analysis

Linear mixed effects models test the effect of experimental variables on metabolism over time. Individual (sample_id_group) is included as a random intercept to account for repeated measures across timepoints. Type III ANOVA with Satterthwaite’s approximation (lmerTest) assesses significance; post-hoc pairwise comparisons use estimated marginal means (emmeans, Tukey adjustment).

run_ts_stats <- function(df, value_col) {
  has_family    <- "family_id_group" %in% names(df) &&
    length(unique(na.omit(df$family_id_group))) > 1
  has_treatment <- "treatment_group" %in% names(df) &&
    length(unique(na.omit(df$treatment_group))) > 1

  if (!has_family && !has_treatment) return(NULL)

  df <- df %>%
    filter(is.finite(.data[[value_col]]), is.finite(time_hr)) %>%
    mutate(
      time_f     = factor(time_hr),
      individual = factor(trace_id)
    )

  if (nrow(df) == 0) return(NULL)

  if (has_family)    df <- df %>% mutate(family    = factor(family_id_group))
  if (has_treatment) df <- df %>% mutate(treatment = factor(treatment_group))

  if (has_family    && length(unique(na.omit(df$family)))    < 2) return(NULL)
  if (has_treatment && length(unique(na.omit(df$treatment))) < 2) return(NULL)

  fixed <- if (has_family && has_treatment) {
    paste0(value_col, " ~ time_f * family * treatment")
  } else if (has_family) {
    paste0(value_col, " ~ time_f * family")
  } else {
    paste0(value_col, " ~ time_f * treatment")
  }

  model <- lmer(
    as.formula(paste0(fixed, " + (1 | individual)")),
    data = df
  )

  anova_res <- anova(model, type = 3, ddf = "Satterthwaite")

  # Pairwise comparisons of group combinations at each timepoint
  emm_spec <- if (has_family && has_treatment) {
    ~ family * treatment | time_f
  } else if (has_family) {
    ~ family | time_f
  } else {
    ~ treatment | time_f
  }

  emm       <- emmeans(model, emm_spec)
  pairs_res <- as.data.frame(pairs(emm, adjust = "tukey"))

  # Main-effect marginal means (collapsed across time)
  emm_main <- if (has_family && has_treatment) {
    emmeans(model, ~ family * treatment)
  } else if (has_family) {
    emmeans(model, ~ family)
  } else {
    emmeans(model, ~ treatment)
  }

  pairs_main <- as.data.frame(pairs(emm_main, adjust = "tukey"))

  list(
    model         = model,
    anova         = anova_res,
    pairs_by_time = pairs_res,
    pairs_main    = pairs_main,
    has_family    = has_family,
    has_treatment = has_treatment
  )
}

ts_stats <- list()
if (nrow(metabolism_df) > 0) {
  for (mc in active_meas_cols) {
    col <- paste0("metabolism_per_", mc)
    if (col %in% names(metabolism_df))
      ts_stats[[col]] <- run_ts_stats(metabolism_df, col)
  }
}

10.1 Results

for (col in names(ts_stats)) {
  res <- ts_stats[[col]]
  if (is.null(res)) next

  cat("\n\n### Metric:", col, "\n\n")

  cat("**Type III ANOVA (Satterthwaite approximation):**\n\n")
  cat(knitr::kable(as.data.frame(res$anova), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")

  cat("**Marginal means – main effects (collapsed across time):**\n\n")
  cat(knitr::kable(as.data.frame(res$pairs_main), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")

  cat("**Pairwise comparisons by timepoint (Tukey):**\n\n")
  cat(knitr::kable(as.data.frame(res$pairs_by_time), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")
}

10.1.1 Metric: metabolism_per_area_mm2_measurement

Type III ANOVA (Satterthwaite approximation):

Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
time_f 0.0087 0.0022 4 360 202.7653 0.0000
family 0.0004 0.0000 8 90 4.2254 0.0002
time_f:family 0.0005 0.0000 32 360 1.4954 0.0445

Marginal means – main effects (collapsed across time):

contrast estimate SE df t.ratio p.value
1 - 10 0.0007 0.0011 90 0.6033 0.9996
1 - 3 0.0016 0.0011 90 1.4286 0.8838
1 - 5 -0.0008 0.0011 90 -0.7160 0.9984
1 - 6 -0.0010 0.0011 90 -0.8495 0.9949
1 - 7 0.0030 0.0011 90 2.6946 0.1646
1 - 8 0.0003 0.0011 90 0.2789 1.0000
1 - 9 -0.0025 0.0011 90 -2.2032 0.4127
1 - 9b -0.0012 0.0011 90 -1.0365 0.9812
10 - 3 0.0009 0.0011 90 0.8253 0.9958
10 - 5 -0.0015 0.0011 90 -1.3193 0.9230
10 - 6 -0.0016 0.0011 90 -1.4528 0.8737
10 - 7 0.0024 0.0011 90 2.0913 0.4857
10 - 8 -0.0004 0.0011 90 -0.3244 1.0000
10 - 9 -0.0032 0.0011 90 -2.8065 0.1279
10 - 9b -0.0019 0.0011 90 -1.6398 0.7803
3 - 5 -0.0024 0.0011 90 -2.1446 0.4505
3 - 6 -0.0026 0.0011 90 -2.2781 0.3663
3 - 7 0.0014 0.0011 90 1.2660 0.9385
3 - 8 -0.0013 0.0011 90 -1.1497 0.9646
3 - 9 -0.0041 0.0011 90 -3.6318 0.0132
3 - 9b -0.0028 0.0011 90 -2.4651 0.2631
5 - 6 -0.0002 0.0011 90 -0.1336 1.0000
5 - 7 0.0039 0.0011 90 3.4106 0.0259
5 - 8 0.0011 0.0011 90 0.9949 0.9855
5 - 9 -0.0017 0.0011 90 -1.4872 0.8586
5 - 9b -0.0004 0.0011 90 -0.3205 1.0000
6 - 7 0.0040 0.0011 90 3.5441 0.0174
6 - 8 0.0013 0.0011 90 1.1284 0.9683
6 - 9 -0.0015 0.0011 90 -1.3536 0.9117
6 - 9b -0.0002 0.0011 90 -0.1870 1.0000
7 - 8 -0.0027 0.0011 90 -2.4157 0.2885
7 - 9 -0.0055 0.0011 90 -4.8978 0.0001
7 - 9b -0.0042 0.0011 90 -3.7311 0.0097
8 - 9 -0.0028 0.0011 90 -2.4820 0.2547
8 - 9b -0.0015 0.0011 90 -1.3154 0.9242
9 - 9b 0.0013 0.0011 90 1.1667 0.9614

Pairwise comparisons by timepoint (Tukey):

contrast time_f estimate SE df t.ratio p.value
1 - 10 0 0.0000 0.0017 322.9919 -0.0113 1.0000
1 - 3 0 0.0001 0.0017 322.9919 0.0782 1.0000
1 - 5 0 -0.0004 0.0017 322.9919 -0.2384 1.0000
1 - 6 0 -0.0003 0.0017 322.9919 -0.1523 1.0000
1 - 7 0 0.0000 0.0017 322.9919 0.0126 1.0000
1 - 8 0 -0.0002 0.0017 322.9919 -0.0925 1.0000
1 - 9 0 -0.0003 0.0017 322.9919 -0.1746 1.0000
1 - 9b 0 -0.0003 0.0017 322.9919 -0.1637 1.0000
10 - 3 0 0.0002 0.0017 322.9919 0.0895 1.0000
10 - 5 0 -0.0004 0.0017 322.9919 -0.2271 1.0000
10 - 6 0 -0.0002 0.0017 322.9919 -0.1409 1.0000
10 - 7 0 0.0000 0.0017 322.9919 0.0239 1.0000
10 - 8 0 -0.0001 0.0017 322.9919 -0.0811 1.0000
10 - 9 0 -0.0003 0.0017 322.9919 -0.1632 1.0000
10 - 9b 0 -0.0003 0.0017 322.9919 -0.1523 1.0000
3 - 5 0 -0.0005 0.0017 322.9919 -0.3166 1.0000
3 - 6 0 -0.0004 0.0017 322.9919 -0.2305 1.0000
3 - 7 0 -0.0001 0.0017 322.9919 -0.0656 1.0000
3 - 8 0 -0.0003 0.0017 322.9919 -0.1707 1.0000
3 - 9 0 -0.0004 0.0017 322.9919 -0.2528 1.0000
3 - 9b 0 -0.0004 0.0017 322.9919 -0.2419 1.0000
5 - 6 0 0.0001 0.0017 322.9919 0.0861 1.0000
5 - 7 0 0.0004 0.0017 322.9919 0.2510 1.0000
5 - 8 0 0.0002 0.0017 322.9919 0.1459 1.0000
5 - 9 0 0.0001 0.0017 322.9919 0.0639 1.0000
5 - 9b 0 0.0001 0.0017 322.9919 0.0747 1.0000
6 - 7 0 0.0003 0.0017 322.9919 0.1648 1.0000
6 - 8 0 0.0001 0.0017 322.9919 0.0598 1.0000
6 - 9 0 0.0000 0.0017 322.9919 -0.0223 1.0000
6 - 9b 0 0.0000 0.0017 322.9919 -0.0114 1.0000
7 - 8 0 -0.0002 0.0017 322.9919 -0.1050 1.0000
7 - 9 0 -0.0003 0.0017 322.9919 -0.1871 1.0000
7 - 9b 0 -0.0003 0.0017 322.9919 -0.1763 1.0000
8 - 9 0 -0.0001 0.0017 322.9919 -0.0821 1.0000
8 - 9b 0 -0.0001 0.0017 322.9919 -0.0712 1.0000
9 - 9b 0 0.0000 0.0017 322.9919 0.0109 1.0000
1 - 10 1 0.0007 0.0017 322.9919 0.4104 1.0000
1 - 3 1 -0.0001 0.0017 322.9919 -0.0776 1.0000
1 - 5 1 -0.0025 0.0017 322.9919 -1.4836 0.8626
1 - 6 1 -0.0012 0.0017 322.9919 -0.7249 0.9984
1 - 7 1 0.0021 0.0017 322.9919 1.2320 0.9489
1 - 8 1 0.0003 0.0017 322.9919 0.1696 1.0000
1 - 9 1 -0.0026 0.0017 322.9919 -1.5752 0.8177
1 - 9b 1 -0.0005 0.0017 322.9919 -0.2856 1.0000
10 - 3 1 -0.0008 0.0017 322.9919 -0.4880 0.9999
10 - 5 1 -0.0032 0.0017 322.9919 -1.8940 0.6184
10 - 6 1 -0.0019 0.0017 322.9919 -1.1353 0.9684
10 - 7 1 0.0014 0.0017 322.9919 0.8215 0.9962
10 - 8 1 -0.0004 0.0017 322.9919 -0.2408 1.0000
10 - 9 1 -0.0033 0.0017 322.9919 -1.9856 0.5546
10 - 9b 1 -0.0012 0.0017 322.9919 -0.6960 0.9988
3 - 5 1 -0.0024 0.0017 322.9919 -1.4060 0.8950
3 - 6 1 -0.0011 0.0017 322.9919 -0.6473 0.9993
3 - 7 1 0.0022 0.0017 322.9919 1.3096 0.9281
3 - 8 1 0.0004 0.0017 322.9919 0.2472 1.0000
3 - 9 1 -0.0025 0.0017 322.9919 -1.4976 0.8562
3 - 9b 1 -0.0003 0.0017 322.9919 -0.2080 1.0000
5 - 6 1 0.0013 0.0017 322.9919 0.7587 0.9978
5 - 7 1 0.0046 0.0017 322.9919 2.7155 0.1465
5 - 8 1 0.0028 0.0017 322.9919 1.6531 0.7744
5 - 9 1 -0.0002 0.0017 322.9919 -0.0916 1.0000
5 - 9b 1 0.0020 0.0017 322.9919 1.1980 0.9565
6 - 7 1 0.0033 0.0017 322.9919 1.9569 0.5747
6 - 8 1 0.0015 0.0017 322.9919 0.8945 0.9932
6 - 9 1 -0.0014 0.0017 322.9919 -0.8503 0.9952
6 - 9b 1 0.0007 0.0017 322.9919 0.4393 1.0000
7 - 8 1 -0.0018 0.0017 322.9919 -1.0624 0.9790
7 - 9 1 -0.0047 0.0017 322.9919 -2.8071 0.1173
7 - 9b 1 -0.0026 0.0017 322.9919 -1.5175 0.8468
8 - 9 1 -0.0029 0.0017 322.9919 -1.7447 0.7182
8 - 9b 1 -0.0008 0.0017 322.9919 -0.4551 1.0000
9 - 9b 1 0.0022 0.0017 322.9919 1.2896 0.9339
1 - 10 2 0.0000 0.0017 322.9919 0.0181 1.0000
1 - 3 2 0.0026 0.0017 322.9919 1.5329 0.8393
1 - 5 2 -0.0017 0.0017 322.9919 -0.9836 0.9872
1 - 6 2 -0.0006 0.0017 322.9919 -0.3311 1.0000
1 - 7 2 0.0027 0.0017 322.9919 1.6254 0.7903
1 - 8 2 0.0006 0.0017 322.9919 0.3483 1.0000
1 - 9 2 -0.0032 0.0017 322.9919 -1.9082 0.6086
1 - 9b 2 -0.0022 0.0017 322.9919 -1.2857 0.9350
10 - 3 2 0.0025 0.0017 322.9919 1.5148 0.8481
10 - 5 2 -0.0017 0.0017 322.9919 -1.0017 0.9856
10 - 6 2 -0.0006 0.0017 322.9919 -0.3492 1.0000
10 - 7 2 0.0027 0.0017 322.9919 1.6073 0.8004
10 - 8 2 0.0006 0.0017 322.9919 0.3302 1.0000
10 - 9 2 -0.0032 0.0017 322.9919 -1.9263 0.5960
10 - 9b 2 -0.0022 0.0017 322.9919 -1.3038 0.9298
3 - 5 2 -0.0042 0.0017 322.9919 -2.5164 0.2280
3 - 6 2 -0.0031 0.0017 322.9919 -1.8640 0.6390
3 - 7 2 0.0002 0.0017 322.9919 0.0925 1.0000
3 - 8 2 -0.0020 0.0017 322.9919 -1.1846 0.9593
3 - 9 2 -0.0058 0.0017 322.9919 -3.4411 0.0187
3 - 9b 2 -0.0047 0.0017 322.9919 -2.8186 0.1140
5 - 6 2 0.0011 0.0017 322.9919 0.6524 0.9993
5 - 7 2 0.0044 0.0017 322.9919 2.6090 0.1869
5 - 8 2 0.0022 0.0017 322.9919 1.3318 0.9211
5 - 9 2 -0.0016 0.0017 322.9919 -0.9247 0.9915
5 - 9b 2 -0.0005 0.0017 322.9919 -0.3021 1.0000
6 - 7 2 0.0033 0.0017 322.9919 1.9565 0.5749
6 - 8 2 0.0011 0.0017 322.9919 0.6794 0.9990
6 - 9 2 -0.0027 0.0017 322.9919 -1.5771 0.8167
6 - 9b 2 -0.0016 0.0017 322.9919 -0.9546 0.9895
7 - 8 2 -0.0021 0.0017 322.9919 -1.2772 0.9374
7 - 9 2 -0.0059 0.0017 322.9919 -3.5337 0.0137
7 - 9b 2 -0.0049 0.0017 322.9919 -2.9111 0.0899
8 - 9 2 -0.0038 0.0017 322.9919 -2.2565 0.3720
8 - 9b 2 -0.0027 0.0017 322.9919 -1.6340 0.7854
9 - 9b 2 0.0010 0.0017 322.9919 0.6225 0.9995
1 - 10 3 0.0015 0.0017 322.9919 0.9149 0.9920
1 - 3 3 0.0044 0.0017 322.9919 2.5913 0.1943
1 - 5 3 0.0000 0.0017 322.9919 -0.0190 1.0000
1 - 6 3 -0.0005 0.0017 322.9919 -0.2939 1.0000
1 - 7 3 0.0049 0.0017 322.9919 2.9145 0.0891
1 - 8 3 0.0004 0.0017 322.9919 0.2360 1.0000
1 - 9 3 -0.0030 0.0017 322.9919 -1.7960 0.6849
1 - 9b 3 -0.0015 0.0017 322.9919 -0.8634 0.9946
10 - 3 3 0.0028 0.0017 322.9919 1.6764 0.7606
10 - 5 3 -0.0016 0.0017 322.9919 -0.9338 0.9909
10 - 6 3 -0.0020 0.0017 322.9919 -1.2088 0.9542
10 - 7 3 0.0034 0.0017 322.9919 1.9996 0.5448
10 - 8 3 -0.0011 0.0017 322.9919 -0.6789 0.9990
10 - 9 3 -0.0046 0.0017 322.9919 -2.7109 0.1481
10 - 9b 3 -0.0030 0.0017 322.9919 -1.7783 0.6966
3 - 5 3 -0.0044 0.0017 322.9919 -2.6103 0.1864
3 - 6 3 -0.0049 0.0017 322.9919 -2.8852 0.0961
3 - 7 3 0.0005 0.0017 322.9919 0.3232 1.0000
3 - 8 3 -0.0040 0.0017 322.9919 -2.3553 0.3126
3 - 9 3 -0.0074 0.0017 322.9919 -4.3873 0.0005
3 - 9b 3 -0.0058 0.0017 322.9919 -3.4547 0.0179
5 - 6 3 -0.0005 0.0017 322.9919 -0.2750 1.0000
5 - 7 3 0.0049 0.0017 322.9919 2.9335 0.0847
5 - 8 3 0.0004 0.0017 322.9919 0.2550 1.0000
5 - 9 3 -0.0030 0.0017 322.9919 -1.7770 0.6974
5 - 9b 3 -0.0014 0.0017 322.9919 -0.8444 0.9954
6 - 7 3 0.0054 0.0017 322.9919 3.2084 0.0388
6 - 8 3 0.0009 0.0017 322.9919 0.5299 0.9998
6 - 9 3 -0.0025 0.0017 322.9919 -1.5021 0.8541
6 - 9b 3 -0.0010 0.0017 322.9919 -0.5695 0.9997
7 - 8 3 -0.0045 0.0017 322.9919 -2.6785 0.1597
7 - 9 3 -0.0079 0.0017 322.9919 -4.7105 0.0001
7 - 9b 3 -0.0064 0.0017 322.9919 -3.7779 0.0058
8 - 9 3 -0.0034 0.0017 322.9919 -2.0320 0.5222
8 - 9b 3 -0.0018 0.0017 322.9919 -1.0994 0.9740
9 - 9b 3 0.0016 0.0017 322.9919 0.9326 0.9910
1 - 10 4 0.0012 0.0017 322.9919 0.6934 0.9988
1 - 3 4 0.0011 0.0017 322.9919 0.6713 0.9991
1 - 5 4 0.0005 0.0017 322.9919 0.3208 1.0000
1 - 6 4 -0.0023 0.0017 322.9919 -1.3499 0.9152
1 - 7 4 0.0055 0.0017 322.9919 3.2618 0.0330
1 - 8 4 0.0005 0.0017 322.9919 0.2749 1.0000
1 - 9 4 -0.0033 0.0017 322.9919 -1.9425 0.5847
1 - 9b 4 -0.0015 0.0017 322.9919 -0.8814 0.9938
10 - 3 4 0.0000 0.0017 322.9919 -0.0221 1.0000
10 - 5 4 -0.0006 0.0017 322.9919 -0.3725 1.0000
10 - 6 4 -0.0034 0.0017 322.9919 -2.0432 0.5143
10 - 7 4 0.0043 0.0017 322.9919 2.5684 0.2042
10 - 8 4 -0.0007 0.0017 322.9919 -0.4185 1.0000
10 - 9 4 -0.0044 0.0017 322.9919 -2.6359 0.1760
10 - 9b 4 -0.0026 0.0017 322.9919 -1.5748 0.8179
3 - 5 4 -0.0006 0.0017 322.9919 -0.3505 1.0000
3 - 6 4 -0.0034 0.0017 322.9919 -2.0212 0.5297
3 - 7 4 0.0044 0.0017 322.9919 2.5905 0.1947
3 - 8 4 -0.0007 0.0017 322.9919 -0.3964 1.0000
3 - 9 4 -0.0044 0.0017 322.9919 -2.6138 0.1849
3 - 9b 4 -0.0026 0.0017 322.9919 -1.5527 0.8294
5 - 6 4 -0.0028 0.0017 322.9919 -1.6707 0.7640
5 - 7 4 0.0049 0.0017 322.9919 2.9410 0.0830
5 - 8 4 -0.0001 0.0017 322.9919 -0.0459 1.0000
5 - 9 4 -0.0038 0.0017 322.9919 -2.2633 0.3677
5 - 9b 4 -0.0020 0.0017 322.9919 -1.2022 0.9556
6 - 7 4 0.0078 0.0017 322.9919 4.6116 0.0002
6 - 8 4 0.0027 0.0017 322.9919 1.6247 0.7906
6 - 9 4 -0.0010 0.0017 322.9919 -0.5927 0.9996
6 - 9b 4 0.0008 0.0017 322.9919 0.4685 0.9999
7 - 8 4 -0.0050 0.0017 322.9919 -2.9869 0.0733
7 - 9 4 -0.0088 0.0017 322.9919 -5.2043 0.0000
7 - 9b 4 -0.0070 0.0017 322.9919 -4.1432 0.0014
8 - 9 4 -0.0037 0.0017 322.9919 -2.2174 0.3969
8 - 9b 4 -0.0019 0.0017 322.9919 -1.1563 0.9648
9 - 9b 4 0.0018 0.0017 322.9919 1.0611 0.9792

11 Area Under the Curve (AUC)

AUC computed per individual via the trapezoid rule across all timepoints. metabolism_per_* is the primary metric matching the paper; corrected_fc and raw_fluorescence are retained for reference.

compute_auc <- function(df, value_col, group_vars) {
  df %>%
    filter(is.finite(time_hr), is.finite(.data[[value_col]])) %>%
    group_by(across(all_of(group_vars))) %>%
    summarise(
      AUC          = trapezoid_auc(time_hr, .data[[value_col]]),
      n_timepoints = n(),
      .groups      = "drop"
    ) %>%
    filter(is.finite(AUC))
}

# Only include grouping columns that are actually present in the data
individual_vars <- intersect(
  c("trace_id", "family_id_group", "treatment_group"),
  names(metabolism_df)
)

auc_metab_list <- list()
if (nrow(metabolism_df) > 0) {
  for (mc in active_meas_cols) {
    col <- paste0("metabolism_per_", mc)
    if (col %in% names(metabolism_df)) {
      auc_metab_list[[col]] <-
        compute_auc(metabolism_df, col, individual_vars) %>%
        mutate(metric = col)
    }
  }
}

auc_all <- bind_rows(auc_metab_list)

str(auc_all)
tibble [99 × 6] (S3: tbl_df/tbl/data.frame)
 $ trace_id       : chr [1:99] "1" "10" "11" "12" ...
 $ family_id_group: chr [1:99] "6" "6" "6" "5" ...
 $ treatment_group: chr [1:99] NA NA NA NA ...
 $ AUC            : num [1:99] 0.0254 0.0245 0.0643 0.0235 0.026 ...
 $ n_timepoints   : int [1:99] 5 5 5 5 5 5 5 5 5 5 ...
 $ metric         : chr [1:99] "metabolism_per_area_mm2_measurement" "metabolism_per_area_mm2_measurement" "metabolism_per_area_mm2_measurement" "metabolism_per_area_mm2_measurement" ...

11.1 AUC summary tables

sum_vars <- intersect(
  c("metric", "family_id_group", "treatment_group"),
  names(auc_all)
)
auc_summary <- auc_all %>%
  group_by(across(all_of(sum_vars))) %>%
  summarise(
    n      = n(),
    mean   = mean(AUC, na.rm = TRUE),
    sd     = sd(AUC, na.rm = TRUE),
    se     = sd / sqrt(n),
    median = median(AUC, na.rm = TRUE),
    .groups = "drop"
  )

print(auc_summary)
# A tibble: 9 × 8
  metric     family_id_group treatment_group     n   mean      sd      se median
  <chr>      <chr>           <chr>           <int>  <dbl>   <dbl>   <dbl>  <dbl>
1 metabolis… 1               <NA>               11 0.0299 0.00717 0.00216 0.0301
2 metabolis… 10              <NA>               11 0.0271 0.00575 0.00173 0.0251
3 metabolis… 3               <NA>               11 0.0225 0.00944 0.00285 0.0210
4 metabolis… 5               <NA>               11 0.0340 0.0154  0.00464 0.0288
5 metabolis… 6               <NA>               11 0.0334 0.0115  0.00347 0.0320
6 metabolis… 7               <NA>               11 0.0175 0.00458 0.00138 0.0163
7 metabolis… 8               <NA>               11 0.0285 0.00379 0.00114 0.0281
8 metabolis… 9               <NA>               11 0.0406 0.0134  0.00404 0.0387
9 metabolis… 9b              <NA>               11 0.0349 0.0128  0.00384 0.0296

12 Statistical Analysis

Each individual oyster (sample_id_group) is the observational unit. The model is built from whichever grouping factors are present: both family and treatment (with interaction) when both exist, or a one-way model when only one factor is available. Each plate maps to a unique family × treatment combination, so plate-level and group-level variance are confounded; interpret accordingly.

run_auc_stats <- function(auc_df) {
  empty <- tibble()

  has_family    <- "family_id_group" %in% names(auc_df) &&
    length(unique(na.omit(auc_df$family_id_group))) > 1
  has_treatment <- "treatment_group" %in% names(auc_df) &&
    length(unique(na.omit(auc_df$treatment_group))) > 1

  if (!has_family && !has_treatment) {
    return(list(model = NULL, anova = NULL,
                pairs_full = empty, pairs_family = empty,
                pairs_trt = empty,
                has_family = FALSE, has_treatment = FALSE))
  }

  if (has_family)    auc_df <- auc_df %>% mutate(family    = factor(family_id_group))
  if (has_treatment) auc_df <- auc_df %>% mutate(treatment = factor(treatment_group))

  formula_str <- if (has_family && has_treatment) {
    "AUC ~ family * treatment"
  } else if (has_family) {
    "AUC ~ family"
  } else {
    "AUC ~ treatment"
  }
  model     <- lm(as.formula(formula_str), data = auc_df)
  anova_res <- anova(model)

  if (has_family && has_treatment) {
    pairs_full   <- as.data.frame(pairs(emmeans(model, ~ family * treatment),
                                        adjust = "tukey"))
    pairs_family <- as.data.frame(pairs(emmeans(model, ~ family),
                                        adjust = "tukey"))
    pairs_trt    <- as.data.frame(pairs(emmeans(model, ~ treatment),
                                        adjust = "tukey"))
  } else if (has_family) {
    pairs_family <- as.data.frame(pairs(emmeans(model, ~ family),
                                        adjust = "tukey"))
    pairs_full   <- pairs_family
    pairs_trt    <- empty
  } else {
    pairs_trt    <- as.data.frame(pairs(emmeans(model, ~ treatment),
                                        adjust = "tukey"))
    pairs_full   <- pairs_trt
    pairs_family <- empty
  }

  list(
    model         = model,
    anova         = anova_res,
    pairs_full    = pairs_full,
    pairs_family  = pairs_family,
    pairs_trt     = pairs_trt,
    has_family    = has_family,
    has_treatment = has_treatment
  )
}

metrics_to_test <- unique(auc_all$metric)
stats_results   <- map(
  set_names(metrics_to_test),
  ~ run_auc_stats(auc_all %>% filter(metric == .x))
)

12.1 Results by metric

for (met in metrics_to_test) {
  stats <- stats_results[[met]]
  cat("\n\n### Metric:", met, "\n\n")
  cat("**ANOVA:**\n\n")
  cat(knitr::kable(as.data.frame(stats$anova), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")
  if (stats$has_family && stats$has_treatment) {
    cat("**Pairwise: family × treatment (Tukey):**\n\n")
    cat(knitr::kable(as.data.frame(stats$pairs_full), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")
    cat("**Pairwise: family main effect:**\n\n")
    cat(knitr::kable(as.data.frame(stats$pairs_family), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")
    cat("**Pairwise: treatment main effect:**\n\n")
    cat(knitr::kable(as.data.frame(stats$pairs_trt), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")
  } else if (stats$has_family) {
    cat("**Pairwise: family (Tukey):**\n\n")
    cat(knitr::kable(as.data.frame(stats$pairs_family), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")
  } else if (stats$has_treatment) {
    cat("**Pairwise: treatment (Tukey):**\n\n")
    cat(knitr::kable(as.data.frame(stats$pairs_trt), digits = 4, format = "pipe"), sep = "\n")
  cat("\n")
  }
}

12.1.1 Metric: metabolism_per_area_mm2_measurement

ANOVA:

Df Sum Sq Mean Sq F value Pr(>F)
family 8 0.0043 5e-04 5.2199 0
Residuals 90 0.0092 1e-04 NA NA

Pairwise: family (Tukey):

contrast estimate SE df t.ratio p.value
1 - 10 0.0028 0.0043 90 0.6569 0.9992
1 - 3 0.0074 0.0043 90 1.7244 0.7301
1 - 5 -0.0041 0.0043 90 -0.9535 0.9890
1 - 6 -0.0035 0.0043 90 -0.8194 0.9960
1 - 7 0.0125 0.0043 90 2.8896 0.1051
1 - 8 0.0014 0.0043 90 0.3296 1.0000
1 - 9 -0.0107 0.0043 90 -2.4718 0.2598
1 - 9b -0.0050 0.0043 90 -1.1533 0.9639
10 - 3 0.0046 0.0043 90 1.0674 0.9774
10 - 5 -0.0069 0.0043 90 -1.6105 0.7966
10 - 6 -0.0064 0.0043 90 -1.4763 0.8635
10 - 7 0.0096 0.0043 90 2.2327 0.3941
10 - 8 -0.0014 0.0043 90 -0.3274 1.0000
10 - 9 -0.0135 0.0043 90 -3.1288 0.0572
10 - 9b -0.0078 0.0043 90 -1.8103 0.6753
3 - 5 -0.0115 0.0043 90 -2.6779 0.1707
3 - 6 -0.0110 0.0043 90 -2.5438 0.2257
3 - 7 0.0050 0.0043 90 1.1652 0.9617
3 - 8 -0.0060 0.0043 90 -1.3948 0.8970
3 - 9 -0.0181 0.0043 90 -4.1962 0.0020
3 - 9b -0.0124 0.0043 90 -2.8777 0.1082
5 - 6 0.0006 0.0043 90 0.1341 1.0000
5 - 7 0.0166 0.0043 90 3.8431 0.0067
5 - 8 0.0055 0.0043 90 1.2831 0.9338
5 - 9 -0.0065 0.0043 90 -1.5183 0.8441
5 - 9b -0.0009 0.0043 90 -0.1998 1.0000
6 - 7 0.0160 0.0043 90 3.7090 0.0104
6 - 8 0.0050 0.0043 90 1.1490 0.9647
6 - 9 -0.0071 0.0043 90 -1.6524 0.7730
6 - 9b -0.0014 0.0043 90 -0.3339 1.0000
7 - 8 -0.0110 0.0043 90 -2.5600 0.2185
7 - 9 -0.0231 0.0043 90 -5.3614 0.0000
7 - 9b -0.0174 0.0043 90 -4.0429 0.0034
8 - 9 -0.0121 0.0043 90 -2.8014 0.1295
8 - 9b -0.0064 0.0043 90 -1.4829 0.8605
9 - 9b 0.0057 0.0043 90 1.3185 0.9232

13 AUC Box Plots with Statistical Annotations

13.1 Significance labels

Significance labels: *** p < 0.001, ** p < 0.01, * p < 0.05. Brackets are drawn only for significant pairs (p < 0.05). Plots are generated for whichever grouping factors are present: treatment-only, family-only, all-groups, within-family, and within-treatment.

sig_label <- function(p) {
  case_when(p < 0.001 ~ "***", p < 0.01 ~ "**", p < 0.05 ~ "*",
            TRUE ~ "ns")
}

# Add significance brackets to an existing ggplot.
# pairs_df   : data frame with $contrast and $p.value columns
# group_levels: ordered character vector matching x-axis factor levels
# y_vals     : numeric vector of AUC values used to set bracket heights
add_sig_brackets <- function(p, pairs_df, group_levels, y_vals) {
  sig_pairs <- pairs_df %>%
    mutate(label = sig_label(p.value)) %>%
    filter(label != "ns")
  if (nrow(sig_pairs) == 0) return(p)

  y_max   <- max(y_vals, na.rm = TRUE)
  y_range <- diff(range(y_vals, na.rm = TRUE))
  step    <- y_range * 0.12

  for (i in seq_len(nrow(sig_pairs))) {
    parts <- str_split(as.character(sig_pairs$contrast[i]), " - ", 2)[[1]]
    g1 <- trimws(parts[1])
    g2 <- trimws(parts[2])
    x1 <- match(g1, group_levels)
    x2 <- match(g2, group_levels)
    if (is.na(x1) || is.na(x2)) next
    bar_y <- y_max + i * step
    p <- p +
      annotate("segment", x = x1, xend = x2,
               y = bar_y, yend = bar_y,
               colour = "black", linewidth = 0.6) +
      annotate("segment", x = x1, xend = x1,
               y = bar_y, yend = bar_y - step * 0.3,
               colour = "black", linewidth = 0.6) +
      annotate("segment", x = x2, xend = x2,
               y = bar_y, yend = bar_y - step * 0.3,
               colour = "black", linewidth = 0.6) +
      annotate("text", x = (x1 + x2) / 2,
               y = bar_y + step * 0.15,
               label = sig_pairs$label[i], size = 4.5)
  }
  p
}

13.2 AUC Boxplots

for (met in metrics_to_test) {
  df      <- auc_all %>% filter(metric == met)
  stats   <- stats_results[[met]]
  y_lab   <- auc_y_label(met)
  has_fam <- stats$has_family
  has_trt <- stats$has_treatment

  # ── Treatment main effect (x = treatment, tick = treatment name) ───────
  if (has_trt) {
    df_p <- df %>%
      mutate(x = factor(treatment_group, levels = sort(unique(treatment_group))))
    grps <- levels(df_p$x)
    p <- ggplot(df_p, aes(x = x, y = AUC, fill = x)) +
      geom_boxplot(alpha = 0.6, outlier.shape = NA) +
      geom_jitter(width = 0.15, alpha = 0.4, size = 1.5) +
      scale_fill_manual(values = trt_colours[grps], guide = "none") +
      labs(x = "Treatment", y = y_lab) +
      theme_classic(base_size = 13)
    p <- add_sig_brackets(p, stats$pairs_trt, grps, df_p$AUC)
    print(p)
    ggsave(file.path(fig_dir, paste0("auc_treatment_", met, ".png")),
           p, width = 5, height = 5)
  }

  # ── Family main effect (x = family, tick = family name) ───────────────
  if (has_fam) {
    df_p <- df %>%
      mutate(x = factor(family_id_group, levels = sort(unique(family_id_group))))
    grps <- levels(df_p$x)
    p <- ggplot(df_p, aes(x = x, y = AUC, fill = x)) +
      geom_boxplot(alpha = 0.6, outlier.shape = NA) +
      geom_jitter(width = 0.15, alpha = 0.4, size = 1.5) +
      scale_fill_manual(values = fam_colours[grps], guide = "none") +
      labs(x = "Family", y = y_lab) +
      theme_classic(base_size = 13)
    p <- add_sig_brackets(p, stats$pairs_family, grps, df_p$AUC)
    print(p)
    ggsave(file.path(fig_dir, paste0("auc_family_", met, ".png")),
           p, width = 5, height = 5)
  }

  # Remaining plots require both factors
  if (!has_fam || !has_trt) next

  # ── All family:treatment groups (x = family:treatment) ─────────────────
  # emmeans contrasts use spaces; convert to colon to match tick labels
  pairs_fc <- stats$pairs_full %>%
    mutate(contrast = str_replace_all(
      contrast,
      "([a-z]+) ([a-z]+)( - )([a-z]+) ([a-z]+)",
      "\\1:\\2\\3\\4:\\5"
    ))
  df_p <- df %>%
    mutate(x = factor(
      paste(family_id_group, treatment_group, sep = ":"),
      levels = sort(unique(paste(family_id_group, treatment_group, sep = ":")))
    ))
  grps     <- levels(df_p$x)
  fill_map <- setNames(make_palette(length(grps)), grps)
  p <- ggplot(df_p, aes(x = x, y = AUC, fill = x)) +
    geom_boxplot(alpha = 0.6, outlier.shape = NA) +
    geom_jitter(width = 0.15, alpha = 0.4, size = 1.5) +
    scale_fill_manual(values = fill_map, guide = "none") +
    labs(x = "Family : Treatment", y = y_lab) +
    theme_classic(base_size = 13) +
    theme(axis.text.x = element_text(angle = 20, hjust = 1))
  p <- add_sig_brackets(p, pairs_fc, grps, df_p$AUC)
  print(p)
  ggsave(file.path(fig_dir, paste0("auc_all_groups_", met, ".png")),
         p, width = 6, height = 5)

  # ── Within each family: treatment comparison (x = family:treatment) ────
  # Tick labels are family:treatment so these plots are visually distinct
  # from the treatment main-effect plot above.
  for (fam in sort(unique(df$family_id_group))) {
    df_p <- df %>%
      filter(family_id_group == fam) %>%
      mutate(x = factor(
        paste(family_id_group, treatment_group, sep = ":"),
        levels = sort(unique(paste(family_id_group, treatment_group, sep = ":")))
      ))
    grps     <- levels(df_p$x)
    pairs_sub <- pairs_fc %>%
      filter(str_count(contrast, paste0(fam, ":")) == 2)
    p <- ggplot(df_p, aes(x = x, y = AUC, fill = x)) +
      geom_boxplot(alpha = 0.6, outlier.shape = NA) +
      geom_jitter(width = 0.15, alpha = 0.4, size = 1.5) +
      scale_fill_manual(values = fill_map[grps], guide = "none") +
      labs(x = "Family : Treatment", y = y_lab) +
      theme_classic(base_size = 13)
    p <- add_sig_brackets(p, pairs_sub, grps, df_p$AUC)
    print(p)
    ggsave(file.path(fig_dir, paste0("auc_", fam, "_trt_", met, ".png")),
           p, width = 5, height = 5)
  }

  # ── Within each treatment: family comparison (x = family:treatment) ────
  # Tick labels are family:treatment so these plots are visually distinct
  # from the family main-effect plot above.
  for (trt in sort(unique(df$treatment_group))) {
    df_p <- df %>%
      filter(treatment_group == trt) %>%
      mutate(x = factor(
        paste(family_id_group, treatment_group, sep = ":"),
        levels = sort(unique(paste(family_id_group, treatment_group, sep = ":")))
      ))
    grps      <- levels(df_p$x)
    pairs_sub <- pairs_fc %>%
      filter(str_count(contrast, paste0(":", trt)) == 2)
    p <- ggplot(df_p, aes(x = x, y = AUC, fill = x)) +
      geom_boxplot(alpha = 0.6, outlier.shape = NA) +
      geom_jitter(width = 0.15, alpha = 0.4, size = 1.5) +
      scale_fill_manual(values = fill_map[grps], guide = "none") +
      labs(x = "Family : Treatment", y = y_lab) +
      theme_classic(base_size = 13)
    p <- add_sig_brackets(p, pairs_sub, grps, df_p$AUC)
    print(p)
    ggsave(file.path(fig_dir, paste0("auc_", trt, "_fam_", met, ".png")),
           p, width = 5, height = 5)
  }
}

14 Save Output Data

write_csv(auc_all,      file.path(out_dir, "auc_all_metrics.csv"))
write_csv(auc_summary,  file.path(out_dir, "auc_summary.csv"))

if (nrow(metabolism_df) > 0)
  write_csv(metabolism_df,
            file.path(out_dir, "metabolism.csv"))

stats_compiled <- map_dfr(metrics_to_test, function(met) {
  bind_rows(
    stats_results[[met]]$pairs_full %>%
      mutate(comparison = "family:treatment"),
    stats_results[[met]]$pairs_family %>%
      mutate(comparison = "family"),
    stats_results[[met]]$pairs_trt %>%
      mutate(comparison = "treatment")
  ) %>% mutate(metric = met)
})

write_csv(stats_compiled,
          file.path(out_dir, "pairwise_stats.csv"))

message("Output files written to: ", out_dir)