Resazurin Assays - USDA M.gigas Families in Response to Freshwater Stress

2026
Pacific oyster
resazurin
freshwater 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 room temperature (~21°C) over the course of 28hrs.

METHODS

Resazurin assays

11 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 room temperature (~21°C) for 28hrs, 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:

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RESULTS

Size-normalized metabolic rate (resazurin fluorescence fold-change per mm² body area, integrated as AUC over ~28 h) did not differ significantly among the nine USDA M. gigas families exposed to freshwater stress at room temperature.

No Tukey-adjusted pairwise comparisons on AUC reached significance. Despite the non-significant AUC result, the time-series linear mixed model identified a consistent and strengthening contrast between families 6 and 7 across the later timepoints: the 6 vs. 7 contrast was significant at 22 h (p = 0.018), 24 h (p = 0.004), 26 h (p < 0.001), and 28 h (p < 0.0001). At 28 h, family 6 also differed significantly from families 5 (p = 0.023), 8 (p = 0.008), and 9b (p = 0.023), and family 1 differed from family 7 (p = 0.010).

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 freshwater stress responses at room temperature (20°C). Plates were left at room temperature 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-RT/README.md for full experimental notes.

1.1 Expected inputs

Path Description
Resazurin/data/20260505-mgig-freshwater-RT/plate-*-T*.txt Plate reader fluorescence exports (one file per plate per timepoint)
Resazurin/data/20260505-mgig-freshwater-RT/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-RT/.

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-RT")
out_dir   <- file.path(proj_root, "Resazurin", "outputs",
                        "01.00-resazurin-20260505-mgig-freshwater-RT")
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 [756 × 6] (S3: tbl_df/tbl/data.frame)
 $ row_id  : chr [1:756] "A" "A" "A" "A" ...
 $ col_id  : int [1:756] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id : chr [1:756] "A1" "A2" "A3" "A4" ...
 $ value   : num [1:756] 122 118 118 124 111 114 112 121 120 121 ...
 $ plate_id: chr [1:756] "a" "a" "a" "a" ...
 $ time_hr : num [1:756] 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] "a" "a" "a" "a" ...
 $ 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] 194 170 141 135 107 ...
 $ imagej_id            : chr [1:108] "1" "3" "4" "2" ...
 $ 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 [756 × 15] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:756] "A" "A" "A" "A" ...
 $ col_id               : int [1:756] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:756] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:756] 122 118 118 124 111 114 112 121 120 121 ...
 $ plate_id             : chr [1:756] "a" "a" "a" "a" ...
 $ time_hr              : num [1:756] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:756] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:756] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:756] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:756] "1" "2" "3" "4" ...
 $ treatment_group      : chr [1:756] NA NA NA NA ...
 $ weight_g_measurement : num [1:756] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:756] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:756] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement : num [1:756] 194 170 141 135 107 ...

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 [693 × 16] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:693] "A" "A" "A" "A" ...
 $ col_id               : int [1:693] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:693] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:693] 122 118 118 124 111 114 112 121 120 121 ...
 $ plate_id             : chr [1:693] "a" "a" "a" "a" ...
 $ time_hr              : num [1:693] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:693] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:693] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:693] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:693] "1" "2" "3" "4" ...
 $ treatment_group      : chr [1:693] NA NA NA NA ...
 $ weight_g_measurement : num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement : num [1:693] 194 170 141 135 107 ...
 $ trace_id             : chr [1:693] "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 [756 × 18] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:756] "A" "A" "A" "A" ...
 $ col_id               : int [1:756] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:756] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:756] 122 118 118 124 111 114 112 121 120 121 ...
 $ plate_id             : chr [1:756] "a" "a" "a" "a" ...
 $ time_hr              : num [1:756] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:756] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:756] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:756] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:756] "1" "2" "3" "4" ...
 $ treatment_group      : chr [1:756] NA NA NA NA ...
 $ weight_g_measurement : num [1:756] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:756] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:756] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement : num [1:756] 194 170 141 135 107 ...
 $ sample_key           : chr [1:756] "1" "2" "3" "4" ...
 $ value_t0             : num [1:756] 122 118 118 124 111 114 112 121 120 121 ...
 $ fold_change          : num [1:756] 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 [63 × 4] (S3: tbl_df/tbl/data.frame)
 $ plate_id      : chr [1:63] "a" "a" "a" "a" ...
 $ time_hr       : num [1:63] 0 2 4 22 24 26 28 0 2 4 ...
 $ mean_blank_rfu: num [1:63] 102 106 108 123 119 120 121 101 103 106 ...
 $ mean_blank_fc : num [1:63] 1.01 1.05 1.07 1.22 1.18 ...

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 [693 × 22] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:693] "A" "A" "A" "A" ...
 $ col_id               : int [1:693] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:693] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:693] 122 118 118 124 111 114 112 121 120 121 ...
 $ plate_id             : chr [1:693] "a" "a" "a" "a" ...
 $ time_hr              : num [1:693] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:693] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:693] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:693] "6" "6" "6" "6" ...
 $ sample_id_group      : chr [1:693] "1" "2" "3" "4" ...
 $ treatment_group      : chr [1:693] NA NA NA NA ...
 $ weight_g_measurement : num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement : num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement : num [1:693] 194 170 141 135 107 ...
 $ sample_key           : chr [1:693] "1" "2" "3" "4" ...
 $ value_t0             : num [1:693] 122 118 118 124 111 114 112 121 120 121 ...
 $ fold_change          : num [1:693] 1 1 1 1 1 1 1 1 1 1 ...
 $ trace_id             : chr [1:693] "1" "2" "3" "4" ...
 $ mean_blank_rfu       : num [1:693] 102 102 102 102 102 102 102 102 102 102 ...
 $ mean_blank_fc        : num [1:693] 1.01 1.01 1.01 1.01 1.01 ...
 $ corrected_fc         : num [1:693] -0.0132 -0.0132 -0.0132 -0.0132 -0.0132 ...

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 [693 × 23] (S3: tbl_df/tbl/data.frame)
 $ row_id                             : chr [1:693] "A" "A" "A" "A" ...
 $ col_id                             : int [1:693] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id                            : chr [1:693] "A1" "A2" "A3" "A4" ...
 $ value                              : num [1:693] 122 118 118 124 111 114 112 121 120 121 ...
 $ plate_id                           : chr [1:693] "a" "a" "a" "a" ...
 $ time_hr                            : num [1:693] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank                           : logi [1:693] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis              : logi [1:693] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group                    : chr [1:693] "6" "6" "6" "6" ...
 $ sample_id_group                    : chr [1:693] "1" "2" "3" "4" ...
 $ treatment_group                    : chr [1:693] NA NA NA NA ...
 $ weight_g_measurement               : num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ width_mm_measurement               : num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement              : num [1:693] NA NA NA NA NA NA NA NA NA NA ...
 $ area_mm2_measurement               : num [1:693] 194 170 141 135 107 ...
 $ sample_key                         : chr [1:693] "1" "2" "3" "4" ...
 $ value_t0                           : num [1:693] 122 118 118 124 111 114 112 121 120 121 ...
 $ fold_change                        : num [1:693] 1 1 1 1 1 1 1 1 1 1 ...
 $ trace_id                           : chr [1:693] "1" "2" "3" "4" ...
 $ mean_blank_rfu                     : num [1:693] 102 102 102 102 102 102 102 102 102 102 ...
 $ mean_blank_fc                      : num [1:693] 1.01 1.01 1.01 1.01 1.01 ...
 $ corrected_fc                       : num [1:693] -0.0132 -0.0132 -0.0132 -0.0132 -0.0132 ...
 $ metabolism_per_area_mm2_measurement: num [1:693] -6.84e-05 -7.80e-05 -9.39e-05 -9.85e-05 -1.24e-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 0.1827 0.0305 6 540 262.9905 0.0000
family 0.0015 0.0002 8 90 1.6018 0.1354
time_f:family 0.0097 0.0002 48 540 1.7387 0.0021

Marginal means – main effects (collapsed across time):

contrast estimate SE df t.ratio p.value
1 - 10 0.0029 0.0052 90 0.5476 0.9998
1 - 3 0.0018 0.0052 90 0.3506 1.0000
1 - 5 0.0043 0.0052 90 0.8138 0.9962
1 - 6 -0.0047 0.0052 90 -0.8978 0.9926
1 - 7 0.0125 0.0052 90 2.3726 0.3118
1 - 8 0.0064 0.0052 90 1.2144 0.9513
1 - 9 0.0024 0.0052 90 0.4512 0.9999
1 - 9b 0.0054 0.0052 90 1.0197 0.9830
10 - 3 -0.0010 0.0052 90 -0.1970 1.0000
10 - 5 0.0014 0.0052 90 0.2662 1.0000
10 - 6 -0.0076 0.0052 90 -1.4453 0.8769
10 - 7 0.0096 0.0052 90 1.8250 0.6656
10 - 8 0.0035 0.0052 90 0.6668 0.9991
10 - 9 -0.0005 0.0052 90 -0.0964 1.0000
10 - 9b 0.0025 0.0052 90 0.4721 0.9999
3 - 5 0.0024 0.0052 90 0.4632 0.9999
3 - 6 -0.0066 0.0052 90 -1.2483 0.9431
3 - 7 0.0106 0.0052 90 2.0220 0.5325
3 - 8 0.0045 0.0052 90 0.8638 0.9943
3 - 9 0.0005 0.0052 90 0.1006 1.0000
3 - 9b 0.0035 0.0052 90 0.6691 0.9990
5 - 6 -0.0090 0.0052 90 -1.7115 0.7379
5 - 7 0.0082 0.0052 90 1.5588 0.8240
5 - 8 0.0021 0.0052 90 0.4006 1.0000
5 - 9 -0.0019 0.0052 90 -0.3626 1.0000
5 - 9b 0.0011 0.0052 90 0.2059 1.0000
6 - 7 0.0172 0.0052 90 3.2703 0.0388
6 - 8 0.0111 0.0052 90 2.1122 0.4718
6 - 9 0.0071 0.0052 90 1.3489 0.9133
6 - 9b 0.0101 0.0052 90 1.9175 0.6037
7 - 8 -0.0061 0.0052 90 -1.1582 0.9630
7 - 9 -0.0101 0.0052 90 -1.9214 0.6010
7 - 9b -0.0071 0.0052 90 -1.3529 0.9120
8 - 9 -0.0040 0.0052 90 -0.7632 0.9976
8 - 9b -0.0010 0.0052 90 -0.1947 1.0000
9 - 9b 0.0030 0.0052 90 0.5685 0.9997

Pairwise comparisons by timepoint (Tukey):

contrast time_f estimate SE df t.ratio p.value
1 - 10 0 0.0001 0.0068 230.0413 0.0210 1.0000
1 - 3 0 0.0001 0.0068 230.0413 0.0130 1.0000
1 - 5 0 0.0002 0.0068 230.0413 0.0225 1.0000
1 - 6 0 0.0002 0.0068 230.0413 0.0321 1.0000
1 - 7 0 0.0003 0.0068 230.0413 0.0399 1.0000
1 - 8 0 0.0002 0.0068 230.0413 0.0328 1.0000
1 - 9 0 0.0000 0.0068 230.0413 0.0036 1.0000
1 - 9b 0 0.0000 0.0068 230.0413 0.0027 1.0000
10 - 3 0 -0.0001 0.0068 230.0413 -0.0079 1.0000
10 - 5 0 0.0000 0.0068 230.0413 0.0015 1.0000
10 - 6 0 0.0001 0.0068 230.0413 0.0111 1.0000
10 - 7 0 0.0001 0.0068 230.0413 0.0189 1.0000
10 - 8 0 0.0001 0.0068 230.0413 0.0119 1.0000
10 - 9 0 -0.0001 0.0068 230.0413 -0.0174 1.0000
10 - 9b 0 -0.0001 0.0068 230.0413 -0.0183 1.0000
3 - 5 0 0.0001 0.0068 230.0413 0.0094 1.0000
3 - 6 0 0.0001 0.0068 230.0413 0.0191 1.0000
3 - 7 0 0.0002 0.0068 230.0413 0.0269 1.0000
3 - 8 0 0.0001 0.0068 230.0413 0.0198 1.0000
3 - 9 0 -0.0001 0.0068 230.0413 -0.0094 1.0000
3 - 9b 0 -0.0001 0.0068 230.0413 -0.0103 1.0000
5 - 6 0 0.0001 0.0068 230.0413 0.0096 1.0000
5 - 7 0 0.0001 0.0068 230.0413 0.0174 1.0000
5 - 8 0 0.0001 0.0068 230.0413 0.0104 1.0000
5 - 9 0 -0.0001 0.0068 230.0413 -0.0189 1.0000
5 - 9b 0 -0.0001 0.0068 230.0413 -0.0198 1.0000
6 - 7 0 0.0001 0.0068 230.0413 0.0078 1.0000
6 - 8 0 0.0000 0.0068 230.0413 0.0007 1.0000
6 - 9 0 -0.0002 0.0068 230.0413 -0.0285 1.0000
6 - 9b 0 -0.0002 0.0068 230.0413 -0.0294 1.0000
7 - 8 0 0.0000 0.0068 230.0413 -0.0071 1.0000
7 - 9 0 -0.0002 0.0068 230.0413 -0.0363 1.0000
7 - 9b 0 -0.0003 0.0068 230.0413 -0.0372 1.0000
8 - 9 0 -0.0002 0.0068 230.0413 -0.0292 1.0000
8 - 9b 0 -0.0002 0.0068 230.0413 -0.0301 1.0000
9 - 9b 0 0.0000 0.0068 230.0413 -0.0009 1.0000
1 - 10 2 -0.0004 0.0068 230.0413 -0.0571 1.0000
1 - 3 2 -0.0001 0.0068 230.0413 -0.0178 1.0000
1 - 5 2 -0.0028 0.0068 230.0413 -0.4132 1.0000
1 - 6 2 -0.0009 0.0068 230.0413 -0.1298 1.0000
1 - 7 2 0.0010 0.0068 230.0413 0.1524 1.0000
1 - 8 2 0.0007 0.0068 230.0413 0.0968 1.0000
1 - 9 2 -0.0023 0.0068 230.0413 -0.3389 1.0000
1 - 9b 2 0.0002 0.0068 230.0413 0.0289 1.0000
10 - 3 2 0.0003 0.0068 230.0413 0.0393 1.0000
10 - 5 2 -0.0024 0.0068 230.0413 -0.3561 1.0000
10 - 6 2 -0.0005 0.0068 230.0413 -0.0727 1.0000
10 - 7 2 0.0014 0.0068 230.0413 0.2095 1.0000
10 - 8 2 0.0010 0.0068 230.0413 0.1540 1.0000
10 - 9 2 -0.0019 0.0068 230.0413 -0.2817 1.0000
10 - 9b 2 0.0006 0.0068 230.0413 0.0860 1.0000
3 - 5 2 -0.0027 0.0068 230.0413 -0.3954 1.0000
3 - 6 2 -0.0008 0.0068 230.0413 -0.1120 1.0000
3 - 7 2 0.0011 0.0068 230.0413 0.1702 1.0000
3 - 8 2 0.0008 0.0068 230.0413 0.1146 1.0000
3 - 9 2 -0.0022 0.0068 230.0413 -0.3211 1.0000
3 - 9b 2 0.0003 0.0068 230.0413 0.0467 1.0000
5 - 6 2 0.0019 0.0068 230.0413 0.2835 1.0000
5 - 7 2 0.0038 0.0068 230.0413 0.5656 0.9997
5 - 8 2 0.0034 0.0068 230.0413 0.5101 0.9999
5 - 9 2 0.0005 0.0068 230.0413 0.0744 1.0000
5 - 9b 2 0.0030 0.0068 230.0413 0.4421 1.0000
6 - 7 2 0.0019 0.0068 230.0413 0.2822 1.0000
6 - 8 2 0.0015 0.0068 230.0413 0.2266 1.0000
6 - 9 2 -0.0014 0.0068 230.0413 -0.2091 1.0000
6 - 9b 2 0.0011 0.0068 230.0413 0.1587 1.0000
7 - 8 2 -0.0004 0.0068 230.0413 -0.0556 1.0000
7 - 9 2 -0.0033 0.0068 230.0413 -0.4913 0.9999
7 - 9b 2 -0.0008 0.0068 230.0413 -0.1235 1.0000
8 - 9 2 -0.0029 0.0068 230.0413 -0.4357 1.0000
8 - 9b 2 -0.0005 0.0068 230.0413 -0.0679 1.0000
9 - 9b 2 0.0025 0.0068 230.0413 0.3678 1.0000
1 - 10 4 0.0013 0.0068 230.0413 0.1988 1.0000
1 - 3 4 0.0008 0.0068 230.0413 0.1183 1.0000
1 - 5 4 -0.0025 0.0068 230.0413 -0.3714 1.0000
1 - 6 4 0.0005 0.0068 230.0413 0.0725 1.0000
1 - 7 4 0.0037 0.0068 230.0413 0.5496 0.9998
1 - 8 4 0.0003 0.0068 230.0413 0.0499 1.0000
1 - 9 4 -0.0010 0.0068 230.0413 -0.1540 1.0000
1 - 9b 4 0.0009 0.0068 230.0413 0.1374 1.0000
10 - 3 4 -0.0005 0.0068 230.0413 -0.0806 1.0000
10 - 5 4 -0.0039 0.0068 230.0413 -0.5702 0.9997
10 - 6 4 -0.0009 0.0068 230.0413 -0.1263 1.0000
10 - 7 4 0.0024 0.0068 230.0413 0.3508 1.0000
10 - 8 4 -0.0010 0.0068 230.0413 -0.1489 1.0000
10 - 9 4 -0.0024 0.0068 230.0413 -0.3529 1.0000
10 - 9b 4 -0.0004 0.0068 230.0413 -0.0614 1.0000
3 - 5 4 -0.0033 0.0068 230.0413 -0.4897 0.9999
3 - 6 4 -0.0003 0.0068 230.0413 -0.0457 1.0000
3 - 7 4 0.0029 0.0068 230.0413 0.4313 1.0000
3 - 8 4 -0.0005 0.0068 230.0413 -0.0683 1.0000
3 - 9 4 -0.0018 0.0068 230.0413 -0.2723 1.0000
3 - 9b 4 0.0001 0.0068 230.0413 0.0192 1.0000
5 - 6 4 0.0030 0.0068 230.0413 0.4439 1.0000
5 - 7 4 0.0062 0.0068 230.0413 0.9210 0.9916
5 - 8 4 0.0028 0.0068 230.0413 0.4213 1.0000
5 - 9 4 0.0015 0.0068 230.0413 0.2174 1.0000
5 - 9b 4 0.0034 0.0068 230.0413 0.5088 0.9999
6 - 7 4 0.0032 0.0068 230.0413 0.4771 0.9999
6 - 8 4 -0.0002 0.0068 230.0413 -0.0226 1.0000
6 - 9 4 -0.0015 0.0068 230.0413 -0.2265 1.0000
6 - 9b 4 0.0004 0.0068 230.0413 0.0649 1.0000
7 - 8 4 -0.0034 0.0068 230.0413 -0.4997 0.9999
7 - 9 4 -0.0048 0.0068 230.0413 -0.7036 0.9987
7 - 9b 4 -0.0028 0.0068 230.0413 -0.4122 1.0000
8 - 9 4 -0.0014 0.0068 230.0413 -0.2039 1.0000
8 - 9b 4 0.0006 0.0068 230.0413 0.0875 1.0000
9 - 9b 4 0.0020 0.0068 230.0413 0.2914 1.0000
1 - 10 22 0.0047 0.0068 230.0413 0.6994 0.9988
1 - 3 22 0.0010 0.0068 230.0413 0.1480 1.0000
1 - 5 22 0.0053 0.0068 230.0413 0.7777 0.9974
1 - 6 22 -0.0065 0.0068 230.0413 -0.9692 0.9882
1 - 7 22 0.0168 0.0068 230.0413 2.4883 0.2432
1 - 8 22 0.0074 0.0068 230.0413 1.0915 0.9750
1 - 9 22 0.0032 0.0068 230.0413 0.4744 0.9999
1 - 9b 22 0.0062 0.0068 230.0413 0.9228 0.9915
10 - 3 22 -0.0037 0.0068 230.0413 -0.5514 0.9998
10 - 5 22 0.0005 0.0068 230.0413 0.0783 1.0000
10 - 6 22 -0.0113 0.0068 230.0413 -1.6685 0.7650
10 - 7 22 0.0121 0.0068 230.0413 1.7889 0.6895
10 - 8 22 0.0026 0.0068 230.0413 0.3921 1.0000
10 - 9 22 -0.0015 0.0068 230.0413 -0.2250 1.0000
10 - 9b 22 0.0015 0.0068 230.0413 0.2234 1.0000
3 - 5 22 0.0043 0.0068 230.0413 0.6297 0.9994
3 - 6 22 -0.0075 0.0068 230.0413 -1.1172 0.9712
3 - 7 22 0.0158 0.0068 230.0413 2.3403 0.3226
3 - 8 22 0.0064 0.0068 230.0413 0.9435 0.9901
3 - 9 22 0.0022 0.0068 230.0413 0.3264 1.0000
3 - 9b 22 0.0052 0.0068 230.0413 0.7748 0.9974
5 - 6 22 -0.0118 0.0068 230.0413 -1.7469 0.7167
5 - 7 22 0.0116 0.0068 230.0413 1.7105 0.7396
5 - 8 22 0.0021 0.0068 230.0413 0.3138 1.0000
5 - 9 22 -0.0020 0.0068 230.0413 -0.3033 1.0000
5 - 9b 22 0.0010 0.0068 230.0413 0.1451 1.0000
6 - 7 22 0.0233 0.0068 230.0413 3.4574 0.0184
6 - 8 22 0.0139 0.0068 230.0413 2.0607 0.5029
6 - 9 22 0.0097 0.0068 230.0413 1.4436 0.8796
6 - 9b 22 0.0128 0.0068 230.0413 1.8919 0.6200
7 - 8 22 -0.0094 0.0068 230.0413 -1.3967 0.8982
7 - 9 22 -0.0136 0.0068 230.0413 -2.0139 0.5353
7 - 9b 22 -0.0106 0.0068 230.0413 -1.5655 0.8224
8 - 9 22 -0.0042 0.0068 230.0413 -0.6171 0.9995
8 - 9b 22 -0.0011 0.0068 230.0413 -0.1687 1.0000
9 - 9b 22 0.0030 0.0068 230.0413 0.4484 1.0000
1 - 10 24 0.0049 0.0068 230.0413 0.7196 0.9985
1 - 3 24 0.0024 0.0068 230.0413 0.3558 1.0000
1 - 5 24 0.0077 0.0068 230.0413 1.1357 0.9682
1 - 6 24 -0.0072 0.0068 230.0413 -1.0730 0.9775
1 - 7 24 0.0193 0.0068 230.0413 2.8525 0.1062
1 - 8 24 0.0098 0.0068 230.0413 1.4442 0.8793
1 - 9 24 0.0041 0.0068 230.0413 0.6044 0.9996
1 - 9b 24 0.0081 0.0068 230.0413 1.2041 0.9550
10 - 3 24 -0.0025 0.0068 230.0413 -0.3639 1.0000
10 - 5 24 0.0028 0.0068 230.0413 0.4161 1.0000
10 - 6 24 -0.0121 0.0068 230.0413 -1.7926 0.6871
10 - 7 24 0.0144 0.0068 230.0413 2.1329 0.4536
10 - 8 24 0.0049 0.0068 230.0413 0.7246 0.9984
10 - 9 24 -0.0008 0.0068 230.0413 -0.1152 1.0000
10 - 9b 24 0.0033 0.0068 230.0413 0.4845 0.9999
3 - 5 24 0.0053 0.0068 230.0413 0.7799 0.9973
3 - 6 24 -0.0096 0.0068 230.0413 -1.4288 0.8857
3 - 7 24 0.0169 0.0068 230.0413 2.4968 0.2390
3 - 8 24 0.0074 0.0068 230.0413 1.0885 0.9754
3 - 9 24 0.0017 0.0068 230.0413 0.2486 1.0000
3 - 9b 24 0.0057 0.0068 230.0413 0.8484 0.9952
5 - 6 24 -0.0149 0.0068 230.0413 -2.2087 0.4036
5 - 7 24 0.0116 0.0068 230.0413 1.7168 0.7357
5 - 8 24 0.0021 0.0068 230.0413 0.3085 1.0000
5 - 9 24 -0.0036 0.0068 230.0413 -0.5313 0.9998
5 - 9b 24 0.0005 0.0068 230.0413 0.0684 1.0000
6 - 7 24 0.0265 0.0068 230.0413 3.9255 0.0036
6 - 8 24 0.0170 0.0068 230.0413 2.5172 0.2292
6 - 9 24 0.0113 0.0068 230.0413 1.6774 0.7597
6 - 9b 24 0.0154 0.0068 230.0413 2.2771 0.3604
7 - 8 24 -0.0095 0.0068 230.0413 -1.4083 0.8938
7 - 9 24 -0.0152 0.0068 230.0413 -2.2481 0.3784
7 - 9b 24 -0.0111 0.0068 230.0413 -1.6484 0.7768
8 - 9 24 -0.0057 0.0068 230.0413 -0.8398 0.9955
8 - 9b 24 -0.0016 0.0068 230.0413 -0.2401 1.0000
9 - 9b 24 0.0040 0.0068 230.0413 0.5997 0.9996
1 - 10 26 0.0046 0.0068 230.0413 0.6788 0.9990
1 - 3 26 0.0036 0.0068 230.0413 0.5261 0.9998
1 - 5 26 0.0097 0.0068 230.0413 1.4377 0.8820
1 - 6 26 -0.0085 0.0068 230.0413 -1.2633 0.9408
1 - 7 26 0.0215 0.0068 230.0413 3.1834 0.0430
1 - 8 26 0.0118 0.0068 230.0413 1.7454 0.7177
1 - 9 26 0.0054 0.0068 230.0413 0.8035 0.9967
1 - 9b 26 0.0096 0.0068 230.0413 1.4180 0.8900
10 - 3 26 -0.0010 0.0068 230.0413 -0.1528 1.0000
10 - 5 26 0.0051 0.0068 230.0413 0.7588 0.9978
10 - 6 26 -0.0131 0.0068 230.0413 -1.9421 0.5853
10 - 7 26 0.0169 0.0068 230.0413 2.5045 0.2353
10 - 8 26 0.0072 0.0068 230.0413 1.0666 0.9783
10 - 9 26 0.0008 0.0068 230.0413 0.1247 1.0000
10 - 9b 26 0.0050 0.0068 230.0413 0.7392 0.9982
3 - 5 26 0.0062 0.0068 230.0413 0.9116 0.9922
3 - 6 26 -0.0121 0.0068 230.0413 -1.7894 0.6892
3 - 7 26 0.0179 0.0068 230.0413 2.6573 0.1694
3 - 8 26 0.0082 0.0068 230.0413 1.2194 0.9516
3 - 9 26 0.0019 0.0068 230.0413 0.2775 1.0000
3 - 9b 26 0.0060 0.0068 230.0413 0.8919 0.9932
5 - 6 26 -0.0182 0.0068 230.0413 -2.7010 0.1533
5 - 7 26 0.0118 0.0068 230.0413 1.7457 0.7175
5 - 8 26 0.0021 0.0068 230.0413 0.3078 1.0000
5 - 9 26 -0.0043 0.0068 230.0413 -0.6341 0.9994
5 - 9b 26 -0.0001 0.0068 230.0413 -0.0197 1.0000
6 - 7 26 0.0300 0.0068 230.0413 4.4467 0.0005
6 - 8 26 0.0203 0.0068 230.0413 3.0087 0.0705
6 - 9 26 0.0140 0.0068 230.0413 2.0668 0.4986
6 - 9b 26 0.0181 0.0068 230.0413 2.6813 0.1604
7 - 8 26 -0.0097 0.0068 230.0413 -1.4379 0.8819
7 - 9 26 -0.0161 0.0068 230.0413 -2.3798 0.3001
7 - 9b 26 -0.0119 0.0068 230.0413 -1.7653 0.7049
8 - 9 26 -0.0064 0.0068 230.0413 -0.9419 0.9903
8 - 9b 26 -0.0022 0.0068 230.0413 -0.3274 1.0000
9 - 9b 26 0.0041 0.0068 230.0413 0.6145 0.9995
1 - 10 28 0.0049 0.0068 230.0413 0.7190 0.9985
1 - 3 28 0.0052 0.0068 230.0413 0.7644 0.9977
1 - 5 28 0.0124 0.0068 230.0413 1.8392 0.6560
1 - 6 28 -0.0105 0.0068 230.0413 -1.5544 0.8281
1 - 7 28 0.0246 0.0068 230.0413 3.6442 0.0099
1 - 8 28 0.0145 0.0068 230.0413 2.1473 0.4440
1 - 9 28 0.0072 0.0068 230.0413 1.0620 0.9789
1 - 9b 28 0.0124 0.0068 230.0413 1.8347 0.6590
10 - 3 28 0.0003 0.0068 230.0413 0.0454 1.0000
10 - 5 28 0.0076 0.0068 230.0413 1.1202 0.9707
10 - 6 28 -0.0154 0.0068 230.0413 -2.2734 0.3627
10 - 7 28 0.0198 0.0068 230.0413 2.9252 0.0881
10 - 8 28 0.0096 0.0068 230.0413 1.4283 0.8859
10 - 9 28 0.0023 0.0068 230.0413 0.3430 1.0000
10 - 9b 28 0.0075 0.0068 230.0413 1.1157 0.9714
3 - 5 28 0.0073 0.0068 230.0413 1.0748 0.9773
3 - 6 28 -0.0157 0.0068 230.0413 -2.3188 0.3352
3 - 7 28 0.0194 0.0068 230.0413 2.8798 0.0991
3 - 8 28 0.0093 0.0068 230.0413 1.3829 0.9033
3 - 9 28 0.0020 0.0068 230.0413 0.2976 1.0000
3 - 9b 28 0.0072 0.0068 230.0413 1.0703 0.9779
5 - 6 28 -0.0229 0.0068 230.0413 -3.3936 0.0226
5 - 7 28 0.0122 0.0068 230.0413 1.8050 0.6789
5 - 8 28 0.0021 0.0068 230.0413 0.3081 1.0000
5 - 9 28 -0.0052 0.0068 230.0413 -0.7772 0.9974
5 - 9b 28 0.0000 0.0068 230.0413 -0.0045 1.0000
6 - 7 28 0.0351 0.0068 230.0413 5.1986 0.0000
6 - 8 28 0.0250 0.0068 230.0413 3.7017 0.0081
6 - 9 28 0.0177 0.0068 230.0413 2.6164 0.1856
6 - 9b 28 0.0229 0.0068 230.0413 3.3891 0.0230
7 - 8 28 -0.0101 0.0068 230.0413 -1.4969 0.8561
7 - 9 28 -0.0174 0.0068 230.0413 -2.5822 0.1999
7 - 9b 28 -0.0122 0.0068 230.0413 -1.8095 0.6759
8 - 9 28 -0.0073 0.0068 230.0413 -1.0853 0.9759
8 - 9b 28 -0.0021 0.0068 230.0413 -0.3126 1.0000
9 - 9b 28 0.0052 0.0068 230.0413 0.7727 0.9975

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] 0.648 0.559 0.575 0.559 0.582 ...
 $ n_timepoints   : int [1:99] 7 7 7 7 7 7 7 7 7 7 ...
 $ 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 metabolism_pe… 1               <NA>               11 0.731 0.492 0.148   0.564
2 metabolism_pe… 10              <NA>               11 0.647 0.214 0.0645  0.557
3 metabolism_pe… 3               <NA>               11 0.696 0.170 0.0512  0.694
4 metabolism_pe… 5               <NA>               11 0.661 0.340 0.103   0.559
5 metabolism_pe… 6               <NA>               11 0.835 0.512 0.154   0.648
6 metabolism_pe… 7               <NA>               11 0.417 0.130 0.0392  0.390
7 metabolism_pe… 8               <NA>               11 0.594 0.167 0.0504  0.584
8 metabolism_pe… 9               <NA>               11 0.687 0.144 0.0433  0.645
9 metabolism_pe… 9b              <NA>               11 0.611 0.363 0.109   0.540

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 1.1337 0.1417 1.4318 0.1942
Residuals 90 8.9077 0.0990 NA NA

Pairwise: family (Tukey):

contrast estimate SE df t.ratio p.value
1 - 10 0.0838 0.1341 90 0.6244 0.9994
1 - 3 0.0349 0.1341 90 0.2602 1.0000
1 - 5 0.0692 0.1341 90 0.5157 0.9999
1 - 6 -0.1041 0.1341 90 -0.7763 0.9973
1 - 7 0.3136 0.1341 90 2.3377 0.3314
1 - 8 0.1362 0.1341 90 1.0152 0.9835
1 - 9 0.0433 0.1341 90 0.3225 1.0000
1 - 9b 0.1198 0.1341 90 0.8931 0.9928
10 - 3 -0.0489 0.1341 90 -0.3643 1.0000
10 - 5 -0.0146 0.1341 90 -0.1087 1.0000
10 - 6 -0.1879 0.1341 90 -1.4007 0.8947
10 - 7 0.2298 0.1341 90 1.7132 0.7369
10 - 8 0.0524 0.1341 90 0.3908 1.0000
10 - 9 -0.0405 0.1341 90 -0.3019 1.0000
10 - 9b 0.0360 0.1341 90 0.2686 1.0000
3 - 5 0.0343 0.1341 90 0.2556 1.0000
3 - 6 -0.1390 0.1341 90 -1.0364 0.9812
3 - 7 0.2787 0.1341 90 2.0775 0.4950
3 - 8 0.1013 0.1341 90 0.7550 0.9977
3 - 9 0.0084 0.1341 90 0.0624 1.0000
3 - 9b 0.0849 0.1341 90 0.6329 0.9994
5 - 6 -0.1733 0.1341 90 -1.2920 0.9312
5 - 7 0.2444 0.1341 90 1.8220 0.6677
5 - 8 0.0670 0.1341 90 0.4995 0.9999
5 - 9 -0.0259 0.1341 90 -0.1932 1.0000
5 - 9b 0.0506 0.1341 90 0.3774 1.0000
6 - 7 0.4177 0.1341 90 3.1139 0.0595
6 - 8 0.2403 0.1341 90 1.7915 0.6876
6 - 9 0.1474 0.1341 90 1.0988 0.9730
6 - 9b 0.2239 0.1341 90 1.6693 0.7632
7 - 8 -0.1774 0.1341 90 -1.3225 0.9220
7 - 9 -0.2703 0.1341 90 -2.0151 0.5372
7 - 9b -0.1938 0.1341 90 -1.4446 0.8772
8 - 9 -0.0929 0.1341 90 -0.6927 0.9988
8 - 9b -0.0164 0.1341 90 -0.1221 1.0000
9 - 9b 0.0765 0.1341 90 0.5705 0.9997

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)