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

2026
Pacific oyster
resazurin
heat stress
Magallana gigas
Crassostrea gigas
Author

Sam White

Published

June 2, 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 48hrs.

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 incubated at room temperature (~21°C), and fluorescence was measured periodically over two days 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 ~48 h) differed significantly among the nine USDA M. gigas families exposed to freshwater stress at room temperature (one-way ANOVA: F(8, 90) = 3.74, p < 0.001).

Tukey-adjusted pairwise comparisons on AUC identified significant differences between family 1 and families 9 (p = 0.024) and 9b (p = 0.012), and between family 8 and family 9b (p = 0.029). In the time-series linear mixed model these contrasts were already evident in the marginal means collapsed across all timepoints (family 1 vs. 9: p = 0.014; family 1 vs. 9b: p = 0.008), with the largest and most numerous significant pairwise differences emerging at the 43.5–48 h timepoints. No wells were excluded from analysis, and all nine plates passed the consistency check (12 wells per plate at every timepoint).

1 Background

Juvenile oysters from nine USDA families were submerged in 4 mL of room temperature resazurin working solution prepared with TAPWATER in 12-well plates and held at room temperature (~21C). At each designated timepoint, fluorescence was measured using a Synergy HTX (Agilent) plate reader.

See Resazurin/data/20260602-mgig-freshwater-RT/README.md for full experimental notes.

1.1 Expected inputs

Path Description
Resazurin/data/20260602-mgig-freshwater-RT/plate-*-T*.txt Plate reader fluorescence exports (one file per plate per timepoint)
Resazurin/data/20260602-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-20260602-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", "20260602-mgig-freshwater-RT")
out_dir   <- file.path(proj_root, "Resazurin", "outputs",
                        "01.00-resazurin-20260602-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] 132 145 141 142 147 133 132 141 123 149 ...
 $ plate_id: chr [1:756] "l" "l" "l" "l" ...
 $ 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 × 14] (S3: tbl_df/tbl/data.frame)
 $ plate_id             : chr [1:108] "l" "l" "l" "l" ...
 $ 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] "9" "5" "7" "9b" ...
 $ sample_id_group      : chr [1:108] "1" "2" "3" "4" ...
 $ exclude_from_analysis: logi [1:108] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ exclude_reason       : chr [1:108] NA NA NA NA ...
 $ 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] 152 194 179 165 205 ...
 $ imagej_id            : chr [1:108] "2" "3" "1" "4" ...
 $ well_id              : chr [1:108] "A1" "A2" "A3" "A4" ...

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 × 16] (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] 132 145 141 142 147 133 132 141 123 149 ...
 $ plate_id             : chr [1:756] "l" "l" "l" "l" ...
 $ 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 ...
 $ exclude_reason       : chr [1:756] NA NA NA NA ...
 $ family_id_group      : chr [1:756] "9" "5" "7" "9b" ...
 $ 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] 152 194 179 165 205 ...

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   <- str_sort(unique(na.omit(raw_df$family_id_group)), numeric = TRUE)
treatments <- sort(unique(na.omit(raw_df$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 × 17] (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] 132 145 141 142 147 133 132 141 123 149 ...
 $ plate_id             : chr [1:693] "l" "l" "l" "l" ...
 $ 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 ...
 $ exclude_reason       : chr [1:693] NA NA NA NA ...
 $ family_id_group      : chr [1:693] "9" "5" "7" "9b" ...
 $ 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] 152 194 179 165 205 ...
 $ 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",
                      breaks = families) +
  scale_fill_manual(values = fam_colours, name = "Family",
                    breaks = families) +
  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",
    breaks = if (has_trt) treatments else families) +
  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",
                        breaks = families) +
    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 × 19] (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] 132 145 141 142 147 133 132 141 123 149 ...
 $ plate_id             : chr [1:756] "l" "l" "l" "l" ...
 $ 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 ...
 $ exclude_reason       : chr [1:756] NA NA NA NA ...
 $ family_id_group      : chr [1:756] "9" "5" "7" "9b" ...
 $ 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] 152 194 179 165 205 ...
 $ sample_key           : chr [1:756] "1" "2" "3" "4" ...
 $ value_t0             : num [1:756] 132 145 141 142 147 133 132 141 123 149 ...
 $ 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] "l" "l" "l" "l" ...
 $ time_hr       : num [1:63] 0 1 2 3 43.5 45 48 0 1 2 ...
 $ mean_blank_rfu: num [1:63] 119 120 118 119 128 127 126 117 117 117 ...
 $ mean_blank_fc : num [1:63] 1.01 1.02 1 1.01 1.09 ...

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 × 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] 132 145 141 142 147 133 132 141 123 149 ...
 $ plate_id             : chr [1:693] "l" "l" "l" "l" ...
 $ 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 ...
 $ exclude_reason       : chr [1:693] NA NA NA NA ...
 $ family_id_group      : chr [1:693] "9" "5" "7" "9b" ...
 $ 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] 152 194 179 165 205 ...
 $ sample_key           : chr [1:693] "1" "2" "3" "4" ...
 $ value_t0             : num [1:693] 132 145 141 142 147 133 132 141 123 149 ...
 $ 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] 119 119 119 119 119 119 119 119 119 119 ...
 $ mean_blank_fc        : num [1:693] 1.01 1.01 1.01 1.01 1.01 ...
 $ corrected_fc         : num [1:693] -0.0123 -0.0123 -0.0123 -0.0123 -0.0123 ...

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",
                      breaks = families) +
  scale_fill_manual(values = fam_colours, name = "Family",
                    breaks = families) +
  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",
    breaks = if (has_trt) treatments else families) +
  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",
                        breaks = families) +
    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 × 24] (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] 132 145 141 142 147 133 132 141 123 149 ...
 $ plate_id                           : chr [1:693] "l" "l" "l" "l" ...
 $ 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 ...
 $ exclude_reason                     : chr [1:693] NA NA NA NA ...
 $ family_id_group                    : chr [1:693] "9" "5" "7" "9b" ...
 $ 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] 152 194 179 165 205 ...
 $ sample_key                         : chr [1:693] "1" "2" "3" "4" ...
 $ value_t0                           : num [1:693] 132 145 141 142 147 133 132 141 123 149 ...
 $ 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] 119 119 119 119 119 119 119 119 119 119 ...
 $ mean_blank_fc                      : num [1:693] 1.01 1.01 1.01 1.01 1.01 ...
 $ corrected_fc                       : num [1:693] -0.0123 -0.0123 -0.0123 -0.0123 -0.0123 ...
 $ metabolism_per_area_mm2_measurement: num [1:693] -8.08e-05 -6.35e-05 -6.88e-05 -7.45e-05 -5.98e-05 ...

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",
                          breaks = families) +
      scale_fill_manual(values = fam_colours, name = "Family",
                        breaks = families) +
      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",
        breaks = if (has_trt) treatments else families) +
      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",
                            breaks = families) +
        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 0.7330 0.1222 6 540 237.8637 0e+00
family 0.0162 0.0020 8 90 3.9535 5e-04
time_f:family 0.0987 0.0021 48 540 4.0036 0e+00

Marginal means – main effects (collapsed across time):

contrast estimate SE df t.ratio p.value
1 - 10 0.0081 0.0078 90 1.0291 0.9820
1 - 3 0.0059 0.0078 90 0.7570 0.9977
1 - 5 0.0224 0.0078 90 2.8554 0.1141
1 - 6 0.0081 0.0078 90 1.0311 0.9818
1 - 7 0.0166 0.0078 90 2.1099 0.4733
1 - 8 0.0034 0.0078 90 0.4390 1.0000
1 - 9 0.0284 0.0078 90 3.6170 0.0139
1 - 9b 0.0298 0.0078 90 3.7932 0.0079
10 - 3 -0.0021 0.0078 90 -0.2721 1.0000
10 - 5 0.0143 0.0078 90 1.8263 0.6648
10 - 6 0.0000 0.0078 90 0.0021 1.0000
10 - 7 0.0085 0.0078 90 1.0809 0.9756
10 - 8 -0.0046 0.0078 90 -0.5901 0.9996
10 - 9 0.0203 0.0078 90 2.5880 0.2064
10 - 9b 0.0217 0.0078 90 2.7641 0.1410
3 - 5 0.0165 0.0078 90 2.0984 0.4810
3 - 6 0.0022 0.0078 90 0.2742 1.0000
3 - 7 0.0106 0.0078 90 1.3529 0.9120
3 - 8 -0.0025 0.0078 90 -0.3180 1.0000
3 - 9 0.0224 0.0078 90 2.8600 0.1128
3 - 9b 0.0238 0.0078 90 3.0362 0.0729
5 - 6 -0.0143 0.0078 90 -1.8243 0.6662
5 - 7 -0.0059 0.0078 90 -0.7455 0.9979
5 - 8 -0.0190 0.0078 90 -2.4164 0.2881
5 - 9 0.0060 0.0078 90 0.7616 0.9976
5 - 9b 0.0074 0.0078 90 0.9378 0.9901
6 - 7 0.0085 0.0078 90 1.0788 0.9759
6 - 8 -0.0046 0.0078 90 -0.5922 0.9996
6 - 9 0.0203 0.0078 90 2.5859 0.2073
6 - 9b 0.0217 0.0078 90 2.7620 0.1417
7 - 8 -0.0131 0.0078 90 -1.6709 0.7623
7 - 9 0.0118 0.0078 90 1.5071 0.8494
7 - 9b 0.0132 0.0078 90 1.6832 0.7550
8 - 9 0.0249 0.0078 90 3.1780 0.0501
8 - 9b 0.0263 0.0078 90 3.3542 0.0305
9 - 9b 0.0014 0.0078 90 0.1761 1.0000

Pairwise comparisons by timepoint (Tukey):

contrast time_f estimate SE df t.ratio p.value
1 - 10 0 0.0000 0.0119 371.3416 0.0023 1.0000
1 - 3 0 0.0000 0.0119 371.3416 0.0002 1.0000
1 - 5 0 0.0000 0.0119 371.3416 0.0025 1.0000
1 - 6 0 0.0001 0.0119 371.3416 0.0055 1.0000
1 - 7 0 0.0000 0.0119 371.3416 0.0035 1.0000
1 - 8 0 0.0001 0.0119 371.3416 0.0045 1.0000
1 - 9 0 0.0000 0.0119 371.3416 0.0013 1.0000
1 - 9b 0 0.0000 0.0119 371.3416 0.0011 1.0000
10 - 3 0 0.0000 0.0119 371.3416 -0.0021 1.0000
10 - 5 0 0.0000 0.0119 371.3416 0.0002 1.0000
10 - 6 0 0.0000 0.0119 371.3416 0.0032 1.0000
10 - 7 0 0.0000 0.0119 371.3416 0.0012 1.0000
10 - 8 0 0.0000 0.0119 371.3416 0.0021 1.0000
10 - 9 0 0.0000 0.0119 371.3416 -0.0010 1.0000
10 - 9b 0 0.0000 0.0119 371.3416 -0.0012 1.0000
3 - 5 0 0.0000 0.0119 371.3416 0.0023 1.0000
3 - 6 0 0.0001 0.0119 371.3416 0.0053 1.0000
3 - 7 0 0.0000 0.0119 371.3416 0.0033 1.0000
3 - 8 0 0.0001 0.0119 371.3416 0.0042 1.0000
3 - 9 0 0.0000 0.0119 371.3416 0.0011 1.0000
3 - 9b 0 0.0000 0.0119 371.3416 0.0009 1.0000
5 - 6 0 0.0000 0.0119 371.3416 0.0030 1.0000
5 - 7 0 0.0000 0.0119 371.3416 0.0010 1.0000
5 - 8 0 0.0000 0.0119 371.3416 0.0020 1.0000
5 - 9 0 0.0000 0.0119 371.3416 -0.0012 1.0000
5 - 9b 0 0.0000 0.0119 371.3416 -0.0014 1.0000
6 - 7 0 0.0000 0.0119 371.3416 -0.0020 1.0000
6 - 8 0 0.0000 0.0119 371.3416 -0.0011 1.0000
6 - 9 0 0.0000 0.0119 371.3416 -0.0042 1.0000
6 - 9b 0 -0.0001 0.0119 371.3416 -0.0044 1.0000
7 - 8 0 0.0000 0.0119 371.3416 0.0010 1.0000
7 - 9 0 0.0000 0.0119 371.3416 -0.0022 1.0000
7 - 9b 0 0.0000 0.0119 371.3416 -0.0024 1.0000
8 - 9 0 0.0000 0.0119 371.3416 -0.0031 1.0000
8 - 9b 0 0.0000 0.0119 371.3416 -0.0033 1.0000
9 - 9b 0 0.0000 0.0119 371.3416 -0.0002 1.0000
1 - 10 1 0.0000 0.0119 371.3416 -0.0016 1.0000
1 - 3 1 0.0002 0.0119 371.3416 0.0145 1.0000
1 - 5 1 0.0000 0.0119 371.3416 0.0018 1.0000
1 - 6 1 0.0005 0.0119 371.3416 0.0391 1.0000
1 - 7 1 0.0004 0.0119 371.3416 0.0359 1.0000
1 - 8 1 0.0000 0.0119 371.3416 -0.0011 1.0000
1 - 9 1 -0.0001 0.0119 371.3416 -0.0042 1.0000
1 - 9b 1 0.0005 0.0119 371.3416 0.0446 1.0000
10 - 3 1 0.0002 0.0119 371.3416 0.0161 1.0000
10 - 5 1 0.0000 0.0119 371.3416 0.0034 1.0000
10 - 6 1 0.0005 0.0119 371.3416 0.0407 1.0000
10 - 7 1 0.0004 0.0119 371.3416 0.0375 1.0000
10 - 8 1 0.0000 0.0119 371.3416 0.0005 1.0000
10 - 9 1 0.0000 0.0119 371.3416 -0.0026 1.0000
10 - 9b 1 0.0005 0.0119 371.3416 0.0462 1.0000
3 - 5 1 -0.0002 0.0119 371.3416 -0.0127 1.0000
3 - 6 1 0.0003 0.0119 371.3416 0.0246 1.0000
3 - 7 1 0.0003 0.0119 371.3416 0.0214 1.0000
3 - 8 1 -0.0002 0.0119 371.3416 -0.0156 1.0000
3 - 9 1 -0.0002 0.0119 371.3416 -0.0188 1.0000
3 - 9b 1 0.0004 0.0119 371.3416 0.0301 1.0000
5 - 6 1 0.0004 0.0119 371.3416 0.0373 1.0000
5 - 7 1 0.0004 0.0119 371.3416 0.0341 1.0000
5 - 8 1 0.0000 0.0119 371.3416 -0.0029 1.0000
5 - 9 1 -0.0001 0.0119 371.3416 -0.0060 1.0000
5 - 9b 1 0.0005 0.0119 371.3416 0.0428 1.0000
6 - 7 1 0.0000 0.0119 371.3416 -0.0032 1.0000
6 - 8 1 -0.0005 0.0119 371.3416 -0.0402 1.0000
6 - 9 1 -0.0005 0.0119 371.3416 -0.0433 1.0000
6 - 9b 1 0.0001 0.0119 371.3416 0.0055 1.0000
7 - 8 1 -0.0004 0.0119 371.3416 -0.0370 1.0000
7 - 9 1 -0.0005 0.0119 371.3416 -0.0402 1.0000
7 - 9b 1 0.0001 0.0119 371.3416 0.0087 1.0000
8 - 9 1 0.0000 0.0119 371.3416 -0.0031 1.0000
8 - 9b 1 0.0005 0.0119 371.3416 0.0457 1.0000
9 - 9b 1 0.0006 0.0119 371.3416 0.0488 1.0000
1 - 10 2 -0.0004 0.0119 371.3416 -0.0356 1.0000
1 - 3 2 0.0002 0.0119 371.3416 0.0178 1.0000
1 - 5 2 0.0003 0.0119 371.3416 0.0276 1.0000
1 - 6 2 0.0008 0.0119 371.3416 0.0630 1.0000
1 - 7 2 0.0012 0.0119 371.3416 0.0979 1.0000
1 - 8 2 0.0000 0.0119 371.3416 0.0026 1.0000
1 - 9 2 0.0002 0.0119 371.3416 0.0206 1.0000
1 - 9b 2 0.0011 0.0119 371.3416 0.0928 1.0000
10 - 3 2 0.0006 0.0119 371.3416 0.0533 1.0000
10 - 5 2 0.0008 0.0119 371.3416 0.0632 1.0000
10 - 6 2 0.0012 0.0119 371.3416 0.0986 1.0000
10 - 7 2 0.0016 0.0119 371.3416 0.1334 1.0000
10 - 8 2 0.0005 0.0119 371.3416 0.0381 1.0000
10 - 9 2 0.0007 0.0119 371.3416 0.0561 1.0000
10 - 9b 2 0.0015 0.0119 371.3416 0.1283 1.0000
3 - 5 2 0.0001 0.0119 371.3416 0.0099 1.0000
3 - 6 2 0.0005 0.0119 371.3416 0.0453 1.0000
3 - 7 2 0.0010 0.0119 371.3416 0.0801 1.0000
3 - 8 2 -0.0002 0.0119 371.3416 -0.0152 1.0000
3 - 9 2 0.0000 0.0119 371.3416 0.0028 1.0000
3 - 9b 2 0.0009 0.0119 371.3416 0.0750 1.0000
5 - 6 2 0.0004 0.0119 371.3416 0.0354 1.0000
5 - 7 2 0.0008 0.0119 371.3416 0.0702 1.0000
5 - 8 2 -0.0003 0.0119 371.3416 -0.0251 1.0000
5 - 9 2 -0.0001 0.0119 371.3416 -0.0071 1.0000
5 - 9b 2 0.0008 0.0119 371.3416 0.0651 1.0000
6 - 7 2 0.0004 0.0119 371.3416 0.0348 1.0000
6 - 8 2 -0.0007 0.0119 371.3416 -0.0605 1.0000
6 - 9 2 -0.0005 0.0119 371.3416 -0.0425 1.0000
6 - 9b 2 0.0004 0.0119 371.3416 0.0297 1.0000
7 - 8 2 -0.0011 0.0119 371.3416 -0.0953 1.0000
7 - 9 2 -0.0009 0.0119 371.3416 -0.0773 1.0000
7 - 9b 2 -0.0001 0.0119 371.3416 -0.0051 1.0000
8 - 9 2 0.0002 0.0119 371.3416 0.0180 1.0000
8 - 9b 2 0.0011 0.0119 371.3416 0.0902 1.0000
9 - 9b 2 0.0009 0.0119 371.3416 0.0722 1.0000
1 - 10 3 -0.0011 0.0119 371.3416 -0.0923 1.0000
1 - 3 3 0.0003 0.0119 371.3416 0.0211 1.0000
1 - 5 3 0.0005 0.0119 371.3416 0.0450 1.0000
1 - 6 3 0.0011 0.0119 371.3416 0.0921 1.0000
1 - 7 3 0.0017 0.0119 371.3416 0.1412 1.0000
1 - 8 3 0.0002 0.0119 371.3416 0.0206 1.0000
1 - 9 3 0.0000 0.0119 371.3416 -0.0040 1.0000
1 - 9b 3 0.0018 0.0119 371.3416 0.1535 1.0000
10 - 3 3 0.0014 0.0119 371.3416 0.1134 1.0000
10 - 5 3 0.0016 0.0119 371.3416 0.1373 1.0000
10 - 6 3 0.0022 0.0119 371.3416 0.1845 1.0000
10 - 7 3 0.0028 0.0119 371.3416 0.2335 1.0000
10 - 8 3 0.0013 0.0119 371.3416 0.1129 1.0000
10 - 9 3 0.0011 0.0119 371.3416 0.0884 1.0000
10 - 9b 3 0.0029 0.0119 371.3416 0.2458 1.0000
3 - 5 3 0.0003 0.0119 371.3416 0.0239 1.0000
3 - 6 3 0.0008 0.0119 371.3416 0.0710 1.0000
3 - 7 3 0.0014 0.0119 371.3416 0.1201 1.0000
3 - 8 3 0.0000 0.0119 371.3416 -0.0005 1.0000
3 - 9 3 -0.0003 0.0119 371.3416 -0.0251 1.0000
3 - 9b 3 0.0016 0.0119 371.3416 0.1324 1.0000
5 - 6 3 0.0006 0.0119 371.3416 0.0471 1.0000
5 - 7 3 0.0011 0.0119 371.3416 0.0962 1.0000
5 - 8 3 -0.0003 0.0119 371.3416 -0.0244 1.0000
5 - 9 3 -0.0006 0.0119 371.3416 -0.0490 1.0000
5 - 9b 3 0.0013 0.0119 371.3416 0.1085 1.0000
6 - 7 3 0.0006 0.0119 371.3416 0.0490 1.0000
6 - 8 3 -0.0009 0.0119 371.3416 -0.0716 1.0000
6 - 9 3 -0.0011 0.0119 371.3416 -0.0961 1.0000
6 - 9b 3 0.0007 0.0119 371.3416 0.0613 1.0000
7 - 8 3 -0.0014 0.0119 371.3416 -0.1206 1.0000
7 - 9 3 -0.0017 0.0119 371.3416 -0.1452 1.0000
7 - 9b 3 0.0001 0.0119 371.3416 0.0123 1.0000
8 - 9 3 -0.0003 0.0119 371.3416 -0.0246 1.0000
8 - 9b 3 0.0016 0.0119 371.3416 0.1329 1.0000
9 - 9b 3 0.0019 0.0119 371.3416 0.1575 1.0000
1 - 10 43.5 0.0148 0.0119 371.3416 1.2408 0.9469
1 - 3 43.5 0.0113 0.0119 371.3416 0.9472 0.9900
1 - 5 43.5 0.0421 0.0119 371.3416 3.5350 0.0135
1 - 6 43.5 0.0129 0.0119 371.3416 1.0867 0.9759
1 - 7 43.5 0.0321 0.0119 371.3416 2.6935 0.1537
1 - 8 43.5 0.0042 0.0119 371.3416 0.3507 1.0000
1 - 9 43.5 0.0572 0.0119 371.3416 4.8084 0.0001
1 - 9b 43.5 0.0600 0.0119 371.3416 5.0379 0.0000
10 - 3 43.5 -0.0035 0.0119 371.3416 -0.2936 1.0000
10 - 5 43.5 0.0273 0.0119 371.3416 2.2941 0.3483
10 - 6 43.5 -0.0018 0.0119 371.3416 -0.1541 1.0000
10 - 7 43.5 0.0173 0.0119 371.3416 1.4527 0.8763
10 - 8 43.5 -0.0106 0.0119 371.3416 -0.8901 0.9934
10 - 9 43.5 0.0425 0.0119 371.3416 3.5676 0.0120
10 - 9b 43.5 0.0452 0.0119 371.3416 3.7971 0.0053
3 - 5 43.5 0.0308 0.0119 371.3416 2.5878 0.1953
3 - 6 43.5 0.0017 0.0119 371.3416 0.1395 1.0000
3 - 7 43.5 0.0208 0.0119 371.3416 1.7463 0.7173
3 - 8 43.5 -0.0071 0.0119 371.3416 -0.5965 0.9996
3 - 9 43.5 0.0460 0.0119 371.3416 3.8612 0.0042
3 - 9b 43.5 0.0487 0.0119 371.3416 4.0907 0.0017
5 - 6 43.5 -0.0291 0.0119 371.3416 -2.4482 0.2613
5 - 7 43.5 -0.0100 0.0119 371.3416 -0.8415 0.9955
5 - 8 43.5 -0.0379 0.0119 371.3416 -3.1843 0.0414
5 - 9 43.5 0.0152 0.0119 371.3416 1.2734 0.9385
5 - 9b 43.5 0.0179 0.0119 371.3416 1.5029 0.8538
6 - 7 43.5 0.0191 0.0119 371.3416 1.6068 0.8008
6 - 8 43.5 -0.0088 0.0119 371.3416 -0.7360 0.9982
6 - 9 43.5 0.0443 0.0119 371.3416 3.7216 0.0070
6 - 9b 43.5 0.0470 0.0119 371.3416 3.9511 0.0030
7 - 8 43.5 -0.0279 0.0119 371.3416 -2.3428 0.3193
7 - 9 43.5 0.0252 0.0119 371.3416 2.1149 0.4647
7 - 9b 43.5 0.0279 0.0119 371.3416 2.3444 0.3184
8 - 9 43.5 0.0531 0.0119 371.3416 4.4577 0.0004
8 - 9b 43.5 0.0558 0.0119 371.3416 4.6872 0.0001
9 - 9b 43.5 0.0027 0.0119 371.3416 0.2295 1.0000
1 - 10 45 0.0192 0.0119 371.3416 1.6134 0.7971
1 - 3 45 0.0137 0.0119 371.3416 1.1505 0.9659
1 - 5 45 0.0525 0.0119 371.3416 4.4101 0.0005
1 - 6 45 0.0183 0.0119 371.3416 1.5367 0.8375
1 - 7 45 0.0374 0.0119 371.3416 3.1459 0.0464
1 - 8 45 0.0074 0.0119 371.3416 0.6214 0.9995
1 - 9 45 0.0666 0.0119 371.3416 5.5918 0.0000
1 - 9b 45 0.0684 0.0119 371.3416 5.7451 0.0000
10 - 3 45 -0.0055 0.0119 371.3416 -0.4629 0.9999
10 - 5 45 0.0333 0.0119 371.3416 2.7967 0.1198
10 - 6 45 -0.0009 0.0119 371.3416 -0.0767 1.0000
10 - 7 45 0.0182 0.0119 371.3416 1.5325 0.8396
10 - 8 45 -0.0118 0.0119 371.3416 -0.9920 0.9865
10 - 9 45 0.0474 0.0119 371.3416 3.9785 0.0027
10 - 9b 45 0.0492 0.0119 371.3416 4.1317 0.0015
3 - 5 45 0.0388 0.0119 371.3416 3.2596 0.0329
3 - 6 45 0.0046 0.0119 371.3416 0.3862 1.0000
3 - 7 45 0.0237 0.0119 371.3416 1.9954 0.5476
3 - 8 45 -0.0063 0.0119 371.3416 -0.5291 0.9998
3 - 9 45 0.0529 0.0119 371.3416 4.4414 0.0004
3 - 9b 45 0.0547 0.0119 371.3416 4.5946 0.0002
5 - 6 45 -0.0342 0.0119 371.3416 -2.8734 0.0986
5 - 7 45 -0.0150 0.0119 371.3416 -1.2642 0.9409
5 - 8 45 -0.0451 0.0119 371.3416 -3.7887 0.0055
5 - 9 45 0.0141 0.0119 371.3416 1.1817 0.9600
5 - 9b 45 0.0159 0.0119 371.3416 1.3350 0.9202
6 - 7 45 0.0192 0.0119 371.3416 1.6092 0.7994
6 - 8 45 -0.0109 0.0119 371.3416 -0.9153 0.9920
6 - 9 45 0.0483 0.0119 371.3416 4.0552 0.0020
6 - 9b 45 0.0501 0.0119 371.3416 4.2084 0.0011
7 - 8 45 -0.0300 0.0119 371.3416 -2.5245 0.2236
7 - 9 45 0.0291 0.0119 371.3416 2.4460 0.2625
7 - 9b 45 0.0309 0.0119 371.3416 2.5992 0.1904
8 - 9 45 0.0592 0.0119 371.3416 4.9704 0.0000
8 - 9b 45 0.0610 0.0119 371.3416 5.1237 0.0000
9 - 9b 45 0.0018 0.0119 371.3416 0.1532 1.0000
1 - 10 48 0.0241 0.0119 371.3416 2.0235 0.5279
1 - 3 48 0.0160 0.0119 371.3416 1.3432 0.9176
1 - 5 48 0.0614 0.0119 371.3416 5.1595 0.0000
1 - 6 48 0.0231 0.0119 371.3416 1.9369 0.5885
1 - 7 48 0.0431 0.0119 371.3416 3.6222 0.0100
1 - 8 48 0.0122 0.0119 371.3416 1.0279 0.9830
1 - 9 48 0.0748 0.0119 371.3416 6.2835 0.0000
1 - 9b 48 0.0766 0.0119 371.3416 6.4355 0.0000
10 - 3 48 -0.0081 0.0119 371.3416 -0.6803 0.9990
10 - 5 48 0.0373 0.0119 371.3416 3.1360 0.0478
10 - 6 48 -0.0010 0.0119 371.3416 -0.0866 1.0000
10 - 7 48 0.0190 0.0119 371.3416 1.5987 0.8052
10 - 8 48 -0.0118 0.0119 371.3416 -0.9956 0.9862
10 - 9 48 0.0507 0.0119 371.3416 4.2600 0.0009
10 - 9b 48 0.0525 0.0119 371.3416 4.4120 0.0005
3 - 5 48 0.0454 0.0119 371.3416 3.8163 0.0049
3 - 6 48 0.0071 0.0119 371.3416 0.5937 0.9996
3 - 7 48 0.0271 0.0119 371.3416 2.2790 0.3576
3 - 8 48 -0.0038 0.0119 371.3416 -0.3153 1.0000
3 - 9 48 0.0588 0.0119 371.3416 4.9403 0.0000
3 - 9b 48 0.0606 0.0119 371.3416 5.0923 0.0000
5 - 6 48 -0.0384 0.0119 371.3416 -3.2226 0.0369
5 - 7 48 -0.0183 0.0119 371.3416 -1.5372 0.8373
5 - 8 48 -0.0492 0.0119 371.3416 -4.1315 0.0015
5 - 9 48 0.0134 0.0119 371.3416 1.1240 0.9703
5 - 9b 48 0.0152 0.0119 371.3416 1.2761 0.9378
6 - 7 48 0.0201 0.0119 371.3416 1.6853 0.7553
6 - 8 48 -0.0108 0.0119 371.3416 -0.9090 0.9924
6 - 9 48 0.0517 0.0119 371.3416 4.3466 0.0006
6 - 9b 48 0.0535 0.0119 371.3416 4.4986 0.0003
7 - 8 48 -0.0309 0.0119 371.3416 -2.5943 0.1925
7 - 9 48 0.0317 0.0119 371.3416 2.6612 0.1656
7 - 9b 48 0.0335 0.0119 371.3416 2.8133 0.1149
8 - 9 48 0.0625 0.0119 371.3416 5.2556 0.0000
8 - 9b 48 0.0644 0.0119 371.3416 5.4076 0.0000
9 - 9b 48 0.0018 0.0119 371.3416 0.1521 1.0000

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] "9" "9" "9b" "9" ...
 $ treatment_group: chr [1:99] NA NA NA NA ...
 $ AUC            : num [1:99] 1.357 0.983 0.274 1.219 0.397 ...
 $ 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_per… 1               <NA>               11 2.30  1.10  0.332  2.09 
2 metabolism_per… 10              <NA>               11 1.93  1.41  0.424  1.42 
3 metabolism_per… 3               <NA>               11 2.00  1.25  0.375  1.84 
4 metabolism_per… 5               <NA>               11 1.19  0.803 0.242  0.860
5 metabolism_per… 6               <NA>               11 1.92  0.596 0.180  1.88 
6 metabolism_per… 7               <NA>               11 1.44  0.997 0.301  1.05 
7 metabolism_per… 8               <NA>               11 2.17  1.32  0.399  1.76 
8 metabolism_per… 9               <NA>               11 0.833 0.546 0.164  0.572
9 metabolism_per… 9b              <NA>               11 0.729 0.456 0.138  0.728

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 29.9274 3.7409 3.7441 8e-04
Residuals 90 89.9242 0.9992 NA NA

Pairwise: family (Tukey):

contrast estimate SE df t.ratio p.value
1 - 10 0.3662 0.4262 90 0.8592 0.9945
1 - 3 0.2971 0.4262 90 0.6971 0.9987
1 - 5 1.1052 0.4262 90 2.5930 0.2043
1 - 6 0.3713 0.4262 90 0.8712 0.9939
1 - 7 0.8586 0.4262 90 2.0144 0.5377
1 - 8 0.1278 0.4262 90 0.2998 1.0000
1 - 9 1.4629 0.4262 90 3.4323 0.0243
1 - 9b 1.5674 0.4262 90 3.6775 0.0115
10 - 3 -0.0691 0.4262 90 -0.1621 1.0000
10 - 5 0.7390 0.4262 90 1.7338 0.7242
10 - 6 0.0051 0.4262 90 0.0120 1.0000
10 - 7 0.4924 0.4262 90 1.1552 0.9636
10 - 8 -0.2385 0.4262 90 -0.5595 0.9997
10 - 9 1.0967 0.4262 90 2.5731 0.2128
10 - 9b 1.2012 0.4262 90 2.8182 0.1245
3 - 5 0.8081 0.4262 90 1.8959 0.6183
3 - 6 0.0742 0.4262 90 0.1741 1.0000
3 - 7 0.5615 0.4262 90 1.3173 0.9236
3 - 8 -0.1694 0.4262 90 -0.3973 1.0000
3 - 9 1.1658 0.4262 90 2.7352 0.1505
3 - 9b 1.2703 0.4262 90 2.9804 0.0840
5 - 6 -0.7338 0.4262 90 -1.7217 0.7317
5 - 7 -0.2466 0.4262 90 -0.5786 0.9997
5 - 8 -0.9774 0.4262 90 -2.2932 0.3573
5 - 9 0.3577 0.4262 90 0.8393 0.9953
5 - 9b 0.4622 0.4262 90 1.0845 0.9751
6 - 7 0.4872 0.4262 90 1.1432 0.9658
6 - 8 -0.2436 0.4262 90 -0.5715 0.9997
6 - 9 1.0916 0.4262 90 2.5610 0.2180
6 - 9b 1.1961 0.4262 90 2.8062 0.1280
7 - 8 -0.7308 0.4262 90 -1.7146 0.7360
7 - 9 0.6043 0.4262 90 1.4179 0.8881
7 - 9b 0.7088 0.4262 90 1.6631 0.7669
8 - 9 1.3351 0.4262 90 3.1325 0.0566
8 - 9b 1.4397 0.4262 90 3.3777 0.0285
9 - 9b 0.1045 0.4262 90 0.2452 1.0000

13 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 = str_sort(unique(family_id_group), numeric = TRUE)))
    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$pairs_full %>%
    mutate(contrast = str_replace_all(
      contrast,
      "([a-z]+) ([a-z]+)( - )([a-z]+) ([a-z]+)",
      "\\1:\\2\\3\\4:\\5"
    ))
  df_p <- df %>%
    mutate(x = factor(
      paste(family_id_group, treatment_group, sep = ":"),
      levels = str_sort(unique(paste(family_id_group, treatment_group, sep = ":")),
                        numeric = TRUE)
    ))
  grps     <- levels(df_p$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 str_sort(unique(df$family_id_group), numeric = TRUE)) {
    df_p <- df %>%
      filter(family_id_group == fam) %>%
      mutate(x = factor(
        paste(family_id_group, treatment_group, sep = ":"),
        levels = str_sort(unique(paste(family_id_group, treatment_group, sep = ":")),
                          numeric = TRUE)
      ))
    grps     <- levels(df_p$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$treatment_group))) {
    df_p <- df %>%
      filter(treatment_group == trt) %>%
      mutate(x = factor(
        paste(family_id_group, treatment_group, sep = ":"),
        levels = str_sort(unique(paste(family_id_group, treatment_group, sep = ":")),
                          numeric = TRUE)
      ))
    grps      <- levels(df_p$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)