Last updated: 2026-07-13

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Knit directory: immgenT-GP-analysis/analysis/

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    Ignored:    figures/final-selected/bits/Figure 6/.DS_Store
    Ignored:    figures/final-selected/bits/Figure 7/.DS_Store
    Ignored:    figures/final-selected/bits/Figure S1/.DS_Store
    Ignored:    figures/final-selected/bits/Figure S2/.DS_Store
    Ignored:    figures/final-selected/bits/Figure S3/.DS_Store
    Ignored:    figures/final-selected/bits/Figure S6/.DS_Store
    Ignored:    figures/final-selected/bits/Figure S7/.DS_Store
    Ignored:    figures/generated/.DS_Store
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    Ignored:    tmp/

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Unstaged changes:
    Modified:   analysis/Figure6.Rmd
    Modified:   analysis/Methods_FlashierFit.Rmd
    Modified:   analysis/assets/Figure6/6b.png
    Modified:   analysis/index.Rmd
    Modified:   figures/generated/Figure 6/6b.pdf
    Modified:   script/Figure3.R
    Modified:   script/Figure6.R

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Figure S4 is produced by script/FigureS4.R. The page provides raw and within-GP-normalized alternatives as clickable tabs for each panel; no display choice has been finalized. The code is shown for reference and is not re-executed on this page. All panels use healthy non-thymocyte cells (condition_broad == "healthy" and annotation_level1 != "thymocyte").

Setup, filtering, and ordering

# Figure S4. Healthy non-thymocyte GP mean-loading heatmaps.
#
# Panel S4a: tissue (organ_simplified).
# Panel S4b: cluster (annotation_level2).
#
# Each panel has raw and within-GP normalized alternatives. Both alternatives
# use the same raw-mean-filtered GP/group set and dominant-group order so they
# can be compared directly. The normalized matrix is calculated before
# filtering, and every retained GP has a raw mean maximum >= 0.1.

suppressPackageStartupMessages({
  library(ComplexHeatmap)
  library(circlize)
  library(grid)
  library(ZemmourLib)
})

if (!file.exists("code/R/setup_data.R")) {
  stop("Run this script from the immgenT-GP-analysis repository root.")
}

source("code/R/setup_data.R")

figure_path <- "figures/generated/Figure S4"
dir.create(figure_path, recursive = TRUE, showWarnings = FALSE)

mean_loading_by_group <- function(L_mat, labels) {
  if (length(labels) != nrow(L_mat) || anyNA(labels) || any(labels == "")) {
    stop("Group labels must be present for every retained cell.")
  }

  labels <- droplevels(factor(as.character(labels)))
  group_sums <- rowsum(L_mat, group = labels, reorder = TRUE)
  group_counts <- as.integer(table(labels)[rownames(group_sums)])

  list(
    matrix = t(sweep(group_sums, 1L, group_counts, "/")),
    counts = data.frame(group = rownames(group_sums), n_cells = group_counts)
  )
}

normalize_by_gp_max <- function(mean_matrix) {
  row_max <- apply(mean_matrix, 1L, max)
  if (any(!is.finite(row_max)) || any(row_max <= 0)) {
    stop("Every GP must have a finite positive maximum mean loading.")
  }
  sweep(mean_matrix, 1L, row_max, "/")
}

filter_raw_mean_matrix <- function(raw_matrix, raw_mean_cutoff) {
  keep_gp <- rowSums(raw_matrix >= raw_mean_cutoff) > 0L
  filtered_raw <- raw_matrix[keep_gp, , drop = FALSE]
  keep_group <- colSums(filtered_raw >= raw_mean_cutoff) > 0L
  filtered_raw <- filtered_raw[, keep_group, drop = FALSE]

  if (nrow(filtered_raw) == 0L || ncol(filtered_raw) < 2L) {
    stop("The raw-mean filter must retain at least one GP and two groups.")
  }
  filtered_raw
}

dominant_group_order <- function(raw_mean_matrix) {
  gp_number <- suppressWarnings(as.integer(sub("^GP", "", rownames(raw_mean_matrix))))
  if (ncol(raw_mean_matrix) < 2L || anyNA(gp_number)) {
    stop("Dominant-group ordering requires at least two groups and GP<number> row names.")
  }

  dominant_index <- max.col(raw_mean_matrix, ties.method = "first")
  dominant_mean <- raw_mean_matrix[cbind(seq_len(nrow(raw_mean_matrix)), dominant_index)]
  second_mean <- apply(raw_mean_matrix, 1L, function(values) sort(values, decreasing = TRUE)[2L])
  dominance_gap <- dominant_mean - second_mean
  dominant_gp_count <- tabulate(dominant_index, nbins = ncol(raw_mean_matrix))
  column_order <- order(-dominant_gp_count, -colMeans(raw_mean_matrix), colnames(raw_mean_matrix))
  dominant_group_position <- match(dominant_index, column_order)
  row_order <- order(dominant_group_position, -dominance_gap, -dominant_mean, gp_number)

  if (any(diff(dominant_group_position[row_order]) < 0L)) {
    stop("Dominant-group blocks are not monotone after ordering.")
  }

  list(row_order = row_order, column_order = column_order)
}

palette_for_groups <- function(groups, palette, label) {
  missing <- setdiff(groups, names(palette))
  if (length(missing) > 0L) {
    stop("The canonical ", label, " palette lacks: ", paste(missing, collapse = ", "))
  }
  palette[groups]
}

render_heatmap <- function(
    matrix,
    group_palette,
    group_label,
    scale_label,
    filename,
    raw_limit,
    row_order,
    column_order,
    raw_mean_cutoff
) {
  if (
    length(row_order) != nrow(matrix) || length(column_order) != ncol(matrix) ||
    !identical(sort(row_order), seq_len(nrow(matrix))) ||
    !identical(sort(column_order), seq_len(ncol(matrix)))
  ) {
    stop("Fixed row and column orders must be complete permutations.")
  }

  if (identical(scale_label, "Raw mean loading")) {
    color_fun <- circlize::colorRamp2(
      c(0, raw_limit * 0.15, raw_limit),
      c("#FFFFFF", "#FCAE91", "#99000D")
    )
    legend_at <- c(0, raw_limit / 2, raw_limit)
  } else {
    color_fun <- circlize::colorRamp2(
      c(0, 0.5, 1),
      c("#FFFFFF", "#FCAE91", "#99000D")
    )
    legend_at <- c(0, 0.5, 1)
  }

  heatmap_width_mm <- max(180, ncol(matrix) * 4.2)
  heatmap_height_mm <- max(480, nrow(matrix) * 3.0)
  pdf_width_in <- (heatmap_width_mm + 130) / 25.4
  pdf_height_in <- (heatmap_height_mm + 120) / 25.4
  order_description <- paste0(
    "dominant-group blocks (within block: dominance gap); focus: raw mean >= ",
    raw_mean_cutoff, " filter"
  )

  column_annotation <- ComplexHeatmap::HeatmapAnnotation(
    group = factor(colnames(matrix), levels = colnames(matrix)),
    col = list(group = group_palette),
    show_legend = FALSE,
    annotation_name_side = "left",
    annotation_height = grid::unit(4, "mm")
  )

  short_scale_label <- if (identical(scale_label, "Raw mean loading")) {
    "Raw GP mean loading"
  } else {
    "Normalized GP mean loading"
  }

  heatmap <- ComplexHeatmap::Heatmap(
    matrix,
    name = scale_label,
    col = color_fun,
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_order = row_order,
    column_order = column_order,
    top_annotation = column_annotation,
    column_title = paste0(short_scale_label, ": ", group_label, "\n", order_description),
    column_title_gp = grid::gpar(fontsize = 13, fontface = "bold"),
    row_title = "GP",
    row_title_gp = grid::gpar(fontsize = 10),
    row_names_gp = grid::gpar(fontsize = 6),
    column_names_gp = grid::gpar(fontsize = 5),
    column_names_rot = 45,
    heatmap_legend_param = list(
      title = scale_label,
      at = legend_at,
      labels = format(legend_at, trim = TRUE, scientific = FALSE)
    ),
    width = grid::unit(heatmap_width_mm, "mm"),
    height = grid::unit(heatmap_height_mm, "mm"),
    use_raster = TRUE,
    raster_quality = 2
  )

  grDevices::pdf(filename, width = pdf_width_in, height = pdf_height_in)
  ComplexHeatmap::draw(
    heatmap,
    heatmap_legend_side = "right",
    padding = grid::unit(c(8, 8, 8, 8), "mm")
  )
  grDevices::dev.off()
}

gp_data <- load_gp_data()
meta <- gp_data$seurat_meta_filtered
healthy_nonthymus <- meta$condition_broad == "healthy" & meta$annotation_level1 != "thymocyte"

if (anyNA(healthy_nonthymus)) {
  stop("Healthy non-thymocyte selection contains missing values.")
}

L_reference <- gp_data$L_pm_filtered[healthy_nonthymus, , drop = FALSE]
meta_reference <- meta[healthy_nonthymus, , drop = FALSE]
if (ncol(L_reference) != 200L || nrow(L_reference) != nrow(meta_reference) || anyNA(L_reference)) {
  stop("The healthy non-thymocyte loading matrix has unexpected dimensions or missing values.")
}

raw_mean_cutoff <- 0.1
organ_result <- mean_loading_by_group(L_reference, meta_reference$organ_simplified)
level2_result <- mean_loading_by_group(L_reference, meta_reference$annotation_level2)
organ_raw <- organ_result$matrix
level2_raw <- level2_result$matrix
organ_normalized <- normalize_by_gp_max(organ_raw)
level2_normalized <- normalize_by_gp_max(level2_raw)
raw_limit <- max(c(organ_raw, level2_raw))

organ_raw_filtered <- filter_raw_mean_matrix(organ_raw, raw_mean_cutoff)
level2_raw_filtered <- filter_raw_mean_matrix(level2_raw, raw_mean_cutoff)
organ_normalized_filtered <- organ_normalized[
  rownames(organ_raw_filtered),
  colnames(organ_raw_filtered),
  drop = FALSE
]
level2_normalized_filtered <- level2_normalized[
  rownames(level2_raw_filtered),
  colnames(level2_raw_filtered),
  drop = FALSE
]
organ_order <- dominant_group_order(organ_raw_filtered)
level2_order <- dominant_group_order(level2_raw_filtered)

stopifnot(
  identical(dimnames(organ_raw_filtered), dimnames(organ_normalized_filtered)),
  identical(dimnames(level2_raw_filtered), dimnames(level2_normalized_filtered)),
  all(apply(organ_raw_filtered, 1L, max) >= raw_mean_cutoff),
  all(apply(level2_raw_filtered, 1L, max) >= raw_mean_cutoff)
)

organ_palette <- palette_for_groups(
  colnames(organ_raw_filtered),
  ZemmourLib::immgent_colors$organ_simplified,
  "organ_simplified"
)
level2_palette <- palette_for_groups(
  colnames(level2_raw_filtered),
  ZemmourLib::immgent_colors$level2,
  "annotation_level2"
)

(a) Tissue mean loading

Both alternatives retain the same 31 GPs and 18 organ_simplified tissues. GPs are retained only when a tissue has raw mean loading >= 0.1; the dominant-group order is calculated on that filtered raw matrix. The normalized values are calculated before filtering, so each displayed GP has a raw mean maximum of at least 0.1.

Raw mean loading

Fig. S4a, raw alternative. Mean GP loading across healthy non-thymocyte tissues. Rows and columns follow dominant-group blocks: each GP is assigned to the tissue with its largest raw mean loading; tissues are ordered by their number of dominant GPs, and GPs within a block by dominance gap.

Within-GP normalized mean loading

Fig. S4a, normalized alternative. The same retained GPs, tissues, and dominant-group order as the raw view, with each GP divided by its largest raw tissue mean before filtering. This view emphasizes relative tissue preference.

(b) Level2 mean loading

Both alternatives retain the same 64 GPs and 107 annotation_level2 clusters. The same raw-mean cutoff and dominant-group ordering rule are used as in panel (a). Every displayed GP has a raw mean maximum of at least 0.1.

Raw mean loading

Fig. S4b, raw alternative. Mean GP loading across healthy non-thymocyte level2 clusters. Clusters and GPs use the filtered dominant-group order.

Within-GP normalized mean loading

Fig. S4b, normalized alternative. The same retained GPs, level2 clusters, and dominant-group order as the raw view, with within-GP normalization applied before filtering.

Panel rendering

render_heatmap(
  organ_raw_filtered,
  organ_palette,
  "tissue (organ_simplified)",
  "Raw mean loading",
  file.path(figure_path, "S4a_raw_mean_loading.pdf"),
  raw_limit,
  organ_order$row_order,
  organ_order$column_order,
  raw_mean_cutoff
)
render_heatmap(
  organ_normalized_filtered,
  organ_palette,
  "tissue (organ_simplified)",
  "Within-GP normalized mean loading",
  file.path(figure_path, "S4a_normalized_mean_loading.pdf"),
  raw_limit,
  organ_order$row_order,
  organ_order$column_order,
  raw_mean_cutoff
)
render_heatmap(
  level2_raw_filtered,
  level2_palette,
  "cluster (annotation_level2)",
  "Raw mean loading",
  file.path(figure_path, "S4b_raw_mean_loading.pdf"),
  raw_limit,
  level2_order$row_order,
  level2_order$column_order,
  raw_mean_cutoff
)
render_heatmap(
  level2_normalized_filtered,
  level2_palette,
  "cluster (annotation_level2)",
  "Within-GP normalized mean loading",
  file.path(figure_path, "S4b_normalized_mean_loading.pdf"),
  raw_limit,
  level2_order$row_order,
  level2_order$column_order,
  raw_mean_cutoff
)

write.csv(
  data.frame(
    panel = c("S4a", "S4b"),
    grouping = c("organ_simplified", "annotation_level2"),
    raw_mean_cutoff = raw_mean_cutoff,
    full_gp_count = c(nrow(organ_raw), nrow(level2_raw)),
    retained_gp_count = c(nrow(organ_raw_filtered), nrow(level2_raw_filtered)),
    full_group_count = c(ncol(organ_raw), ncol(level2_raw)),
    retained_group_count = c(ncol(organ_raw_filtered), ncol(level2_raw_filtered))
  ),
  file.path(figure_path, "S4_filter_summary.csv"),
  row.names = FALSE,
  quote = FALSE
)

message("Wrote Figure S4 heatmaps to ", normalizePath(figure_path))

sessionInfo()
R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.6.1

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8

time zone: Asia/Shanghai
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

loaded via a namespace (and not attached):
 [1] vctrs_0.7.3     cli_3.6.6       knitr_1.50      rlang_1.2.0
 [5] xfun_0.55       stringi_1.8.7   otel_0.2.0      promises_1.5.0
 [9] jsonlite_2.0.0  workflowr_1.7.2 glue_1.8.1      rprojroot_2.1.1
[13] git2r_0.36.2    htmltools_0.5.9 httpuv_1.6.16   sass_0.4.10
[17] rmarkdown_2.30  evaluate_1.0.5  jquerylib_0.1.4 tibble_3.3.0
[21] fastmap_1.2.0   yaml_2.3.12     lifecycle_1.0.5 stringr_1.6.0
[25] compiler_4.5.1  fs_1.6.6        Rcpp_1.1.1-1.1  pkgconfig_2.0.3
[29] later_1.4.4     digest_0.6.39   R6_2.6.1        pillar_1.11.1
[33] magrittr_2.0.5  bslib_0.9.0     tools_4.5.1     cachem_1.1.0