Resazurin Assays - Selected Juvenile Ruditapes philippinarum Exposed to 36C Acute Heat Stress Day 0

INTRO

Steven asked me to perform resazurin assays on “selected” juvenile Ruditapes philippinarum (Manila clam) exposed to 36^oC acute heat stress. The goal of the experiment is to compare metabolic activity between the “selected” clams and control clams when exposed to heat stress.

This is the first day of a two day experiment. Day 0 corresponds to the first day of measurements, and Day 1 corresponds to the second day of measurements. The same clams were measured on both days. Day 1 notebook entry is available here:

• Day 1 Notebook Entry

Data and results are available here:

• https://github.com/RobertsLab/resazurin-assay-development/tree/main/data/clam/20260331-clam-36C-C-vs-S

The section below was knitted from the R Markdown file 00.00-resazurin-20260331-clam-36C-C-vs-S.Rmd (GitHub).

---

1 Background

Control clams (C) compared with selected clams (S) at 36°C using a resazurin metabolic assay. Clams were placed in 12-well plates submerged in 5.0 mL resazurin working solution. Plate layout was randomized. Fluorescence was measured every 30 min on a Synergy HTX (Agilent) plate reader.

Important notes from this experiment:

• Inconsistent heating observed; interior wells were typically ~1.5°C cooler than the rest.
• The following wells showed volume loss (possibly due to clam spitting) and are flagged for exclusion in the layout: Plate A C3, Plate B A3, Plate C C4, Plate D B2.

See data/clam/20260331-clam-36C-C-vs-S/README.md for full experimental notes.

1.1 Expected inputs

Path	Description
data/clam/20260331-clam-36C-C-vs-S/plate-*-T*.txt	Plate reader fluorescence exports (one file per plate per timepoint)
data/clam/20260331-clam-36C-C-vs-S/layout.csv	Well metadata: plate ID, well ID, blank flag, family/treatment groups, size measurements

1.2 Expected outputs

All outputs are written to output/clam/20260331-clam-36C-C-vs-S/.

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

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
)

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, "data", "clam",
                        "20260331-clam-36C-C-vs-S")
fig_dir   <- file.path(proj_root, "output", "clam",
                        "20260331-clam-36C-C-vs-S", "figures")
out_dir   <- file.path(proj_root, "output", "clam",
                        "20260331-clam-36C-C-vs-S")

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 [504 × 6] (S3: tbl_df/tbl/data.frame)
 $ row_id  : chr [1:504] "A" "A" "A" "A" ...
 $ col_id  : int [1:504] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id : chr [1:504] "A1" "A2" "A3" "A4" ...
 $ value   : num [1:504] 280 224 241 219 241 295 255 237 285 270 ...
 $ plate_id: chr [1:504] "a" "a" "a" "a" ...
 $ time_hr : num [1:504] 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 6 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 [72 × 13] (S3: tbl_df/tbl/data.frame)
 $ plate_id             : chr [1:72] "a" "a" "a" "a" ...
 $ plate_well           : chr [1:72] "A01" "A02" "A03" "A04" ...
 $ is_blank             : logi [1:72] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:72] "tweed" "blue" "tweed" "blue" ...
 $ sample_id_group      : chr [1:72] NA NA NA NA ...
 $ treatment_group      : chr [1:72] "selected" "selected" "control" "control" ...
 $ width_mm_measurement : num [1:72] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:72] NA NA NA NA NA NA NA NA NA NA ...
 $ weight_mg_measurement: num [1:72] NA NA NA NA NA NA NA NA NA NA ...
 $ area_cm2_measurement : num [1:72] 1.47 0.847 1.01 0.789 1.062 ...
 $ imagej_id            : chr [1:72] "1" "4" "3" "2" ...
 $ well_id              : chr [1:72] "A1" "A2" "A3" "A4" ...
 $ exclude_from_analysis: logi [1:72] 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 [504 × 15] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:504] "A" "A" "A" "A" ...
 $ col_id               : int [1:504] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:504] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:504] 280 224 241 219 241 295 255 237 285 270 ...
 $ plate_id             : chr [1:504] "a" "a" "a" "a" ...
 $ time_hr              : num [1:504] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:504] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:504] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:504] "tweed" "blue" "tweed" "blue" ...
 $ sample_id_group      : chr [1:504] NA NA NA NA ...
 $ treatment_group      : chr [1:504] "selected" "selected" "control" "control" ...
 $ width_mm_measurement : num [1:504] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:504] NA NA NA NA NA NA NA NA NA NA ...
 $ weight_mg_measurement: num [1:504] NA NA NA NA NA NA NA NA NA NA ...
 $ area_cm2_measurement : num [1:504] 1.47 0.847 1.01 0.789 1.062 ...

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)

str(raw_df)

tibble [462 × 16] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:462] "A" "A" "A" "A" ...
 $ col_id               : int [1:462] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:462] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:462] 280 224 241 219 241 295 255 237 285 270 ...
 $ plate_id             : chr [1:462] "a" "a" "a" "a" ...
 $ time_hr              : num [1:462] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:462] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:462] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:462] "tweed" "blue" "tweed" "blue" ...
 $ sample_id_group      : chr [1:462] NA NA NA NA ...
 $ treatment_group      : chr [1:462] "selected" "selected" "control" "control" ...
 $ width_mm_measurement : num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ weight_mg_measurement: num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ area_cm2_measurement : num [1:462] 1.47 0.847 1.01 0.789 1.062 ...
 $ trace_id             : chr [1:462] "a_A1" "a_A2" "a_A3" "a_A4" ...

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 %>%
  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"
  )

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

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 %>%
  ggplot(aes(x = time_hr, y = value,
             group = trace_id, colour = treatment_group)) +
  geom_line(alpha = 0.6) +
  geom_point(size = 1.2, alpha = 0.7) +
  facet_wrap(~ family_id_group) +
  scale_colour_manual(values = trt_colours, name = "Treatment") +
  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

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

Following Huffmyer et al.: fluorescence is first expressed as fold-change relative to each well’s own T0 reading (applied to samples and blanks alike), the mean fold-change of blank wells (per plate, per timepoint) is then subtracted. All samples therefore start at exactly 0 at T0 by construction, eliminating the risk of negative starting values from pipetting variance.

7.1 Step 1 – Fold-change relative to T0 for all wells

t0_all <- dat %>%
  filter(is.finite(time_hr), is.finite(value)) %>%
  group_by(plate_id, well_id) %>%
  slice_min(time_hr, n = 1, with_ties = FALSE) %>%
  select(plate_id, well_id, value_t0 = value) %>%
  ungroup()

dat_fc <- dat %>%
  left_join(t0_all, by = c("plate_id", "well_id")) %>%
  mutate(fold_change = if_else(
    is.finite(value_t0) & value_t0 > 0,
    value / value_t0,
    NA_real_
  ))

str(dat_fc)

tibble [504 × 17] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:504] "A" "A" "A" "A" ...
 $ col_id               : int [1:504] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:504] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:504] 280 224 241 219 241 295 255 237 285 270 ...
 $ plate_id             : chr [1:504] "a" "a" "a" "a" ...
 $ time_hr              : num [1:504] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:504] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:504] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:504] "tweed" "blue" "tweed" "blue" ...
 $ sample_id_group      : chr [1:504] NA NA NA NA ...
 $ treatment_group      : chr [1:504] "selected" "selected" "control" "control" ...
 $ width_mm_measurement : num [1:504] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:504] NA NA NA NA NA NA NA NA NA NA ...
 $ weight_mg_measurement: num [1:504] NA NA NA NA NA NA NA NA NA NA ...
 $ area_cm2_measurement : num [1:504] 1.47 0.847 1.01 0.789 1.062 ...
 $ value_t0             : num [1:504] 280 224 241 219 241 295 255 237 285 270 ...
 $ fold_change          : num [1:504] 1 1 1 1 1 1 1 1 1 1 ...

7.2 Step 2 – Mean blank fold-change per plate per timepoint

blank_fc_ref <- dat_fc %>%
  filter(is_blank) %>%
  group_by(plate_id, time_hr) %>%
  summarise(mean_blank_fc = mean(fold_change, na.rm = TRUE),
            .groups = "drop")

str(blank_fc_ref)

tibble [42 × 3] (S3: tbl_df/tbl/data.frame)
 $ plate_id     : chr [1:42] "a" "a" "a" "a" ...
 $ time_hr      : num [1:42] 0 0.5 1 1.5 2.5 3 3.5 0 0.5 1 ...
 $ mean_blank_fc: num [1:42] 1 0.995 0.981 0.99 1.005 ...

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 [462 × 20] (S3: tbl_df/tbl/data.frame)
 $ row_id               : chr [1:462] "A" "A" "A" "A" ...
 $ col_id               : int [1:462] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id              : chr [1:462] "A1" "A2" "A3" "A4" ...
 $ value                : num [1:462] 280 224 241 219 241 295 255 237 285 270 ...
 $ plate_id             : chr [1:462] "a" "a" "a" "a" ...
 $ time_hr              : num [1:462] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank             : logi [1:462] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis: logi [1:462] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group      : chr [1:462] "tweed" "blue" "tweed" "blue" ...
 $ sample_id_group      : chr [1:462] NA NA NA NA ...
 $ treatment_group      : chr [1:462] "selected" "selected" "control" "control" ...
 $ width_mm_measurement : num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement: num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ weight_mg_measurement: num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ area_cm2_measurement : num [1:462] 1.47 0.847 1.01 0.789 1.062 ...
 $ value_t0             : num [1:462] 280 224 241 219 241 295 255 237 285 270 ...
 $ fold_change          : num [1:462] 1 1 1 1 1 1 1 1 1 1 ...
 $ trace_id             : chr [1:462] "a_A1" "a_A2" "a_A3" "a_A4" ...
 $ mean_blank_fc        : num [1:462] 1 1 1 1 1 1 1 1 1 1 ...
 $ corrected_fc         : num [1:462] 0 0 0 0 0 0 0 0 0 0 ...

8 Blank-Corrected Fold-Change

8.1 Mean by family

bc_fc_summary <- samples %>%
  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"
  )

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

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 %>%
  ggplot(aes(x = time_hr, y = corrected_fc,
             group = trace_id, colour = treatment_group)) +
  geom_line(alpha = 0.6) +
  geom_point(size = 1.2, alpha = 0.7) +
  facet_wrap(~ family_id_group) +
  scale_colour_manual(values = trt_colours, name = "Treatment") +
  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

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 [462 × 21] (S3: tbl_df/tbl/data.frame)
 $ row_id                             : chr [1:462] "A" "A" "A" "A" ...
 $ col_id                             : int [1:462] 1 2 3 4 1 2 3 4 1 2 ...
 $ well_id                            : chr [1:462] "A1" "A2" "A3" "A4" ...
 $ value                              : num [1:462] 280 224 241 219 241 295 255 237 285 270 ...
 $ plate_id                           : chr [1:462] "a" "a" "a" "a" ...
 $ time_hr                            : num [1:462] 0 0 0 0 0 0 0 0 0 0 ...
 $ is_blank                           : logi [1:462] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_from_analysis              : logi [1:462] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ family_id_group                    : chr [1:462] "tweed" "blue" "tweed" "blue" ...
 $ sample_id_group                    : chr [1:462] NA NA NA NA ...
 $ treatment_group                    : chr [1:462] "selected" "selected" "control" "control" ...
 $ width_mm_measurement               : num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ length_mm_measurement              : num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ weight_mg_measurement              : num [1:462] NA NA NA NA NA NA NA NA NA NA ...
 $ area_cm2_measurement               : num [1:462] 1.47 0.847 1.01 0.789 1.062 ...
 $ value_t0                           : num [1:462] 280 224 241 219 241 295 255 237 285 270 ...
 $ fold_change                        : num [1:462] 1 1 1 1 1 1 1 1 1 1 ...
 $ trace_id                           : chr [1:462] "a_A1" "a_A2" "a_A3" "a_A4" ...
 $ mean_blank_fc                      : num [1:462] 1 1 1 1 1 1 1 1 1 1 ...
 $ corrected_fc                       : num [1:462] 0 0 0 0 0 0 0 0 0 0 ...
 $ metabolism_per_area_cm2_measurement: num [1:462] 0 0 0 0 0 0 0 0 0 0 ...

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 %>%
      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"
      )

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

    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 <- ggplot(metabolism_df,
        aes(x = time_hr, y = .data[[col]],
            group = trace_id, colour = treatment_group)) +
      geom_line(alpha = 0.6) +
      geom_point(size = 1.2, alpha = 0.7) +
      facet_wrap(~ family_id_group) +
      scale_colour_manual(values = trt_colours, name = "Treatment") +
      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)

    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----\n### Metric:", col, "\n\n")

  cat("**Type III ANOVA (Satterthwaite approximation):**\n")
  print(res$anova)

  cat("\n**Marginal means – main effects (collapsed across time):**\n")
  print(res$pairs_main)

  cat("\n**Pairwise comparisons by timepoint (Tukey):**\n")
  print(res$pairs_by_time)
}

### Metric: metabolism_per_area_cm2_measurement
Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
Marginal means – main effects (collapsed across time): contrast estimate SE df t.ratio p.value blue control - tweed control 6.793623 3.174362 62 2.140 0.1519 blue control - blue selected 7.406867 3.222099 62 2.299 0.1094 blue control - tweed selected 8.251319 3.174362 62 2.599 0.0552 tweed control - blue selected 0.613244 3.174362 62 0.193 0.9974 tweed control - tweed selected 1.457696 3.125895 62 0.466 0.9661 blue selected - tweed selected 0.844452 3.174362 62 0.266 0.9933
Results are averaged over the levels of: time_f Degrees-of-freedom method: kenward-roger P value adjustment: tukey method for comparing a family of 4 estimates
Pairwise comparisons by timepoint (Tukey): time_f = 0: contrast estimate SE df t.ratio p.value blue control - tweed control 0.000000 4.036973 152.49 0.000 1.0000 blue control - blue selected 0.000000 4.097683 152.49 0.000 1.0000 blue control - tweed selected 0.000000 4.036973 152.49 0.000 1.0000 tweed control - blue selected 0.000000 4.036973 152.49 0.000 1.0000 tweed control - tweed selected 0.000000 3.975336 152.49 0.000 1.0000 blue selected - tweed selected 0.000000 4.036973 152.49 0.000 1.0000
time_f = 0.5: contrast estimate SE df t.ratio p.value blue control - tweed control 2.890837 4.036973 152.49 0.716 0.8906 blue control - blue selected 2.458084 4.097683 152.49 0.600 0.9320 blue control - tweed selected 2.322904 4.036973 152.49 0.575 0.9393 tweed control - blue selected -0.432752 4.036973 152.49 -0.107 0.9996 tweed control - tweed selected -0.567933 3.975336 152.49 -0.143 0.9990 blue selected - tweed selected -0.135180 4.036973 152.49 -0.033 1.0000
time_f = 1: contrast estimate SE df t.ratio p.value blue control - tweed control 6.929365 4.036973 152.49 1.716 0.3187 blue control - blue selected 5.978033 4.097683 152.49 1.459 0.4649 blue control - tweed selected 5.981791 4.036973 152.49 1.482 0.4510 tweed control - blue selected -0.951332 4.036973 152.49 -0.236 0.9954 tweed control - tweed selected -0.947574 3.975336 152.49 -0.238 0.9952 blue selected - tweed selected 0.003758 4.036973 152.49 0.001 1.0000
time_f = 1.5: contrast estimate SE df t.ratio p.value blue control - tweed control 10.501549 4.036973 152.49 2.601 0.0495 blue control - blue selected 8.139586 4.097683 152.49 1.986 0.1976 blue control - tweed selected 8.705560 4.036973 152.49 2.156 0.1403 tweed control - blue selected -2.361964 4.036973 152.49 -0.585 0.9365 tweed control - tweed selected -1.795990 3.975336 152.49 -0.452 0.9692 blue selected - tweed selected 0.565974 4.036973 152.49 0.140 0.9990
time_f = 2.5: contrast estimate SE df t.ratio p.value blue control - tweed control 10.352552 4.036973 152.49 2.564 0.0544 blue control - blue selected 11.051266 4.097683 152.49 2.697 0.0386 blue control - tweed selected 12.417058 4.036973 152.49 3.076 0.0132 tweed control - blue selected 0.698713 4.036973 152.49 0.173 0.9981 tweed control - tweed selected 2.064506 3.975336 152.49 0.519 0.9543 blue selected - tweed selected 1.365792 4.036973 152.49 0.338 0.9866
time_f = 3: contrast estimate SE df t.ratio p.value blue control - tweed control 9.681967 4.036973 152.49 2.398 0.0817 blue control - blue selected 12.090801 4.097683 152.49 2.951 0.0190 blue control - tweed selected 14.047943 4.036973 152.49 3.480 0.0036 tweed control - blue selected 2.408834 4.036973 152.49 0.597 0.9329 tweed control - tweed selected 4.365976 3.975336 152.49 1.098 0.6911 blue selected - tweed selected 1.957142 4.036973 152.49 0.485 0.9624
time_f = 3.5: contrast estimate SE df t.ratio p.value blue control - tweed control 7.199089 4.036973 152.49 1.783 0.2853 blue control - blue selected 12.130298 4.097683 152.49 2.960 0.0185 blue control - tweed selected 14.283978 4.036973 152.49 3.538 0.0030 tweed control - blue selected 4.931210 4.036973 152.49 1.222 0.6142 tweed control - tweed selected 7.084890 3.975336 152.49 1.782 0.2859 blue selected - tweed selected 2.153680 4.036973 152.49 0.533 0.9508
Degrees-of-freedom method: kenward-roger P value adjustment: tukey method for comparing a family of 4 estimates
# 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.
``` r 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 [66 × 6] (S3: tbl_df/tbl/data.frame) $ trace_id : chr [1:66] "a_A1" "a_A2" "a_A3" "a_A4" ... $ family_id_group: chr [1:66] "tweed" "blue" "tweed" "blue" ... $ treatment_group: chr [1:66] "selected" "selected" "control" "control" ... $ AUC : num [1:66] 34.9 73.3 54.8 78.8 38.4 ... $ n_timepoints : int [1:66] 7 7 7 7 7 7 7 7 7 7 ... $ metric : chr [1:66] "metabolism_per_area_cm2_measurement" "metabolism_per_area_cm2_measurement" "metabolism_per_area_cm2_measurement" "metabolism_per_area_cm2_measurement" ...
## AUC summary tables
``` r 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: 4 × 8 metric family_id_group treatment_group n mean sd se median <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> 1 metabolism_per… blue control 16 56.4 31.7 7.92 59.1 2 metabolism_per… blue selected 16 28.7 27.9 6.99 32.3 3 metabolism_per… tweed control 17 29.2 31.8 7.72 38.8 4 metabolism_per… tweed selected 17 25.8 39.5 9.59 32.1
# Statistical Analysis
Each individual clam (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.
``` r 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)) ) ```
## Results by metric

10.1.1 Metric: metabolism_per_area_cm2_measurement

ANOVA: Analysis of Variance Table

Response: AUC Df Sum Sq Mean Sq F value Pr(>F)
family 1 3732 3731.8 3.4040 0.06982 . treatment 1 3800 3799.9 3.4661 0.06738 . family:treatment 1 2432 2431.8 2.2182 0.14146
Residuals 62 67969 1096.3
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Pairwise: family × treatment (Tukey): contrast estimate SE df t.ratio p.value blue control - tweed control 27.191433 11.53277 62 2.358 0.0962 blue control - blue selected 27.689173 11.70620 62 2.365 0.0946 blue control - tweed selected 30.589277 11.53277 62 2.652 0.0485 tweed control - blue selected 0.497739 11.53277 62 0.043 1.0000 tweed control - tweed selected 3.397844 11.35669 62 0.299 0.9906 blue selected - tweed selected 2.900104 11.53277 62 0.251 0.9944

P value adjustment: tukey method for comparing a family of 4 estimates

Pairwise: family main effect: contrast estimate SE df t.ratio p.value blue - tweed 15.04577 8.154899 62 1.845 0.0698

Results are averaged over the levels of: treatment

Pairwise: treatment main effect: contrast estimate SE df t.ratio p.value control - selected 15.54351 8.154899 62 1.906 0.0613

Results are averaged over the levels of: family

11 AUC Box Plots with Statistical Annotations

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
}

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)
  }
}

12 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)

---

MATERIALS & METHODS

Control clams (C) compared with selected clams (S) at 36^oC.

Clams were processed in 12-well plates and submerged in 5.0mL of resazurin working solution stored at 4^oC,prepared on 20260330 (notebook entry).

Plates were measured every 30mins on a Synergy HTX (Agilent) plate reader.

Plate layout was randomized; Steven is currently the only person who knows the treatment assignments.

Important

• Seemed to be inconsistent heating, with interior wells usually ~1.5^oC lower than rest of wells.

The following wells lost volume - possibly due to clam spitting?

• Plate A, C3
• Plate C, C4
• Plate B, A3
• Plate D, B2

After 3.5hrs, Steven removed the resazurin working solution from the wells and replaced it with 5.0mL of seawater. Plates were returned to the cold room and will be measured again tomorrow.

RESULTS

All six plates passed the consistency check (12 wells per plate at every timepoint). Although four wells were flagged for potential volume loss in the experimental notes (Plate A C3, Plate B A3, Plate C C4, Plate D B2), the layout file did not mark them for exclusion, so no wells were removed from analysis.

Metabolic activity (AUC of blank-corrected, size-normalised fold-change per cm² shell area) showed marginal but non-significant trends for both family (F = 3.40, p = 0.070) and treatment (F = 3.47, p = 0.067).

The only significant pairwise contrast in the AUC analysis was blue control vs. tweed selected (estimate = 30.6, p = 0.048). Blue control clams had the highest mean AUC (56.4 ± 7.9 SE), roughly double that of blue selected (28.7 ± 7.0), tweed control (29.2 ± 7.7), and tweed selected (25.8 ± 9.6).

The time-series mixed-effects model revealed a clear temporal divergence: blue control clams showed progressively higher metabolism than other groups from 1.5 h onward. Significant pairwise differences (Tukey-adjusted) were:

• t = 1.5 h: blue control > tweed control (p = 0.050)
• t = 2.5 h: blue control > blue selected (p = 0.039) and > tweed selected (p = 0.013)
• t = 3.0 h: blue control > blue selected (p = 0.019) and > tweed selected (p = 0.004)
• t = 3.5 h: blue control > blue selected (p = 0.019) and > tweed selected (p = 0.003)

In summary, selected clams did not show higher metabolic activity than control clams under 36°C acute heat stress on Day 0. The dominant pattern was elevated metabolism specifically in blue-family control clams, which diverged from all other groups by mid-assay. The treatment effect (control vs. selected) was not significant in either the AUC or time-series analysis.