Last updated: 2026-07-26

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Knit directory: immgenT-GP-analysis/analysis/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/FigureS4.Rmd) and HTML (docs/FigureS4.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 3c052d6 Ziang Zhang 2026-07-15 Finalize full-centered Figure S4
html 3c052d6 Ziang Zhang 2026-07-15 Finalize full-centered Figure S4
Rmd 8938a27 Ziang Zhang 2026-07-14 Update Figure S4 centered heatmaps
html 8938a27 Ziang Zhang 2026-07-14 Update Figure S4 centered heatmaps
Rmd c344aaa Ziang Zhang 2026-07-13 Add Figure S4 mean-loading heatmaps
html c344aaa Ziang Zhang 2026-07-13 Add Figure S4 mean-loading heatmaps

Figure S4 is produced by script/FigureS4.R. Both panels show the final full row-centered matrices, with all 200 GPs and all observed groups. The analysis uses healthy non-thymocyte cells (condition_broad == "healthy" and annotation_level1 != "thymocyte"). For each GP, its mean loading across groups is subtracted from every group mean. The shared centered color scale is fixed at -0.2 to 0.2; values outside this range are saturated at the endpoint colors.

Setup, centering, and ordering

# Figure S4. Full row-centered healthy non-thymocyte GP mean-loading heatmaps.
#
# Panel S4a: tissue (organ_simplified), all 200 GPs and all tissues.
# Panel S4b: cluster (annotation_level2), all 200 GPs and all clusters.
#
# The shared centered color scale is fixed at [-0.2, 0.2]. Values outside
# this range saturate at the endpoint colors. Level2 columns follow Figure 1's
# level1 order, with level2 labels alphabetized within each level1 block.

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)
  )
}

center_by_gp_mean <- function(mean_matrix) {
  sweep(mean_matrix, 1L, rowMeans(mean_matrix), "-")
}

dominant_group_order <- function(raw_mean_matrix, fixed_column_order = NULL) {
  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

  if (is.null(fixed_column_order)) {
    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))
  } else {
    if (
      length(fixed_column_order) != ncol(raw_mean_matrix) ||
      !identical(sort(fixed_column_order), seq_len(ncol(raw_mean_matrix)))
    ) {
      stop("The fixed column order must be a complete permutation.")
    }
    column_order <- fixed_column_order
  }

  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)
}

level2_to_level1_map <- function(meta, groups, level1_order) {
  mapping <- unique(data.frame(
    group = as.character(meta$annotation_level2),
    level1 = as.character(meta$annotation_level1),
    stringsAsFactors = FALSE
  ))
  if (anyDuplicated(mapping$group)) {
    stop("Each annotation_level2 label must map to exactly one annotation_level1 label.")
  }

  group_level1 <- mapping$level1[match(groups, mapping$group)]
  names(group_level1) <- groups
  if (anyNA(group_level1) || any(!group_level1 %in% level1_order)) {
    stop("Every displayed level2 group must map to the Figure 1 level1 order.")
  }
  group_level1
}

level2_column_order <- function(groups, group_level1, level1_order) {
  order(match(group_level1[groups], level1_order), groups)
}

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_centered_heatmap <- function(
    matrix,
    group_palette,
    group_label,
    filename,
    row_order,
    column_order,
    centered_color_limit,
    order_description,
    group_level1 = NULL,
    level1_palette = NULL
) {
  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.")
  }

  color_fun <- circlize::colorRamp2(
    c(-centered_color_limit, 0, centered_color_limit),
    c("#2166AC", "#FFFFFF", "#B2182B")
  )
  legend_at <- c(-centered_color_limit, 0, centered_color_limit)

  heatmap_width_mm <- max(180, ncol(matrix) * 4.2)
  heatmap_height_mm <- max(160, nrow(matrix) * 3.5)
  pdf_width_in <- (heatmap_width_mm + 130) / 25.4
  pdf_height_in <- (heatmap_height_mm + 90) / 25.4
  cell_width_mm <- heatmap_width_mm / ncol(matrix)
  cell_height_mm <- heatmap_height_mm / nrow(matrix)
  row_label_fontsize <- min(14, max(9, floor(cell_height_mm * 2.8)))
  column_label_fontsize <- min(14, max(9, floor(cell_width_mm * 2.8)))

  if (is.null(group_level1)) {
    column_annotation <- ComplexHeatmap::HeatmapAnnotation(
      group = factor(colnames(matrix), levels = colnames(matrix)),
      col = list(group = group_palette),
      show_legend = FALSE,
      annotation_name_side = "left",
      annotation_name_gp = grid::gpar(fontsize = 10, fontface = "bold"),
      annotation_height = grid::unit(4, "mm")
    )
  } else {
    group_level1 <- group_level1[colnames(matrix)]
    if (anyNA(group_level1) || is.null(level1_palette)) {
      stop("Level2 heatmaps require complete level1 annotations and a palette.")
    }
    column_annotation <- ComplexHeatmap::HeatmapAnnotation(
      level1 = factor(group_level1, levels = names(level1_palette)),
      group = factor(colnames(matrix), levels = colnames(matrix)),
      col = list(level1 = level1_palette, group = group_palette),
      show_legend = FALSE,
      annotation_name_side = "left",
      annotation_name_gp = grid::gpar(fontsize = 10, fontface = "bold"),
      annotation_height = grid::unit(c(4, 4), "mm")
    )
  }

  heatmap <- ComplexHeatmap::Heatmap(
    matrix,
    name = "Row-centered mean loading",
    col = color_fun,
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_order = row_order,
    column_order = column_order,
    top_annotation = column_annotation,
    column_title = paste0(
      "Row-centered GP mean loading: ", group_label, "\n", order_description
    ),
    column_title_gp = grid::gpar(fontsize = 16, fontface = "bold"),
    row_title = "GP",
    row_title_gp = grid::gpar(fontsize = 12),
    row_names_gp = grid::gpar(fontsize = row_label_fontsize),
    column_names_gp = grid::gpar(fontsize = column_label_fontsize),
    column_names_rot = 90,
    heatmap_legend_param = list(
      title = "Row-centered mean loading",
      at = legend_at,
      labels = format(legend_at, trim = TRUE, scientific = FALSE),
      title_gp = grid::gpar(fontsize = 11, fontface = "bold"),
      labels_gp = grid::gpar(fontsize = 10)
    ),
    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.")
}

centered_color_limit <- 0.2
level1_order <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN", "DP")
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_centered <- center_by_gp_mean(organ_raw)
level2_centered <- center_by_gp_mean(level2_raw)

level2_group_level1 <- level2_to_level1_map(
  meta_reference, colnames(level2_raw), level1_order
)
organ_order <- dominant_group_order(organ_raw)
level2_order <- dominant_group_order(
  level2_raw,
  level2_column_order(colnames(level2_raw), level2_group_level1, level1_order)
)

stopifnot(
  nrow(organ_centered) == 200L,
  nrow(level2_centered) == 200L,
  ncol(organ_centered) == 18L,
  ncol(level2_centered) == 107L,
  max(abs(rowMeans(organ_centered))) < 1e-12,
  max(abs(rowMeans(level2_centered))) < 1e-12
)

organ_palette <- palette_for_groups(
  colnames(organ_centered),
  ZemmourLib::immgent_colors$organ_simplified,
  "organ_simplified"
)
level2_palette <- palette_for_groups(
  colnames(level2_centered),
  ZemmourLib::immgent_colors$level2,
  "annotation_level2"
)
level1_palette <- ZemmourLib::immgent_colors$level1[level1_order]

(a) Tissue mean loading

Version Author Date
3c052d6 Ziang Zhang 2026-07-15
8938a27 Ziang Zhang 2026-07-14

Fig. S4a. Row-centered mean GP loading across all 18 organ_simplified tissues for all 200 GPs. 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 each dominant-tissue block are ordered by decreasing dominance gap. The centered color scale is fixed at -0.2 to 0.2.

(b) Level2 mean loading

Version Author Date
3c052d6 Ziang Zhang 2026-07-15
8938a27 Ziang Zhang 2026-07-14

Fig. S4b. Row-centered mean GP loading across all 107 annotation_level2 clusters for all 200 GPs. Columns follow the Figure 1 level1 sequence (CD8, CD4, Treg, gdT, CD8aa, Tz, DN, then DP) and are alphabetized within each level1 block. GPs are arranged into dominant-level2 blocks. The colored top strips show level1 and level2 annotations, and the centered color scale is fixed at -0.2 to 0.2.

Panel rendering

render_centered_heatmap(
  organ_centered,
  organ_palette,
  "tissue (organ_simplified)",
  file.path(figure_path, "S4a_centered_mean_loading.pdf"),
  organ_order$row_order,
  organ_order$column_order,
  centered_color_limit,
  "all 200 GPs; dominant-group blocks (within block: dominance gap)"
)

render_centered_heatmap(
  level2_centered,
  level2_palette,
  "cluster (annotation_level2)",
  file.path(figure_path, "S4b_centered_mean_loading.pdf"),
  level2_order$row_order,
  level2_order$column_order,
  centered_color_limit,
  paste0(
    "all 200 GPs; level2 columns: Figure 1 level1 order ",
    "(CD8, CD4, Treg, gdT, CD8aa, Tz, DN, DP); ",
    "alphabetical within level1; GP rows: dominant-group blocks"
  ),
  group_level1 = level2_group_level1,
  level1_palette = level1_palette
)

summary_dir <- "output/FigureS4"   # build intermediate (not a manuscript panel)
dir.create(summary_dir, recursive = TRUE, showWarnings = FALSE)
write.csv(
  data.frame(
    panel = c("S4a", "S4b"),
    grouping = c("organ_simplified", "annotation_level2"),
    view = "full row-centered mean loading",
    gp_count = c(nrow(organ_centered), nrow(level2_centered)),
    group_count = c(ncol(organ_centered), ncol(level2_centered)),
    centered_definition = "group mean minus mean across groups for each GP",
    color_min = -centered_color_limit,
    color_mid = 0,
    color_max = centered_color_limit,
    observed_min = c(min(organ_centered), min(level2_centered)),
    observed_max = c(max(organ_centered), max(level2_centered))
  ),
  file.path(summary_dir, "S4_summary.csv"),
  row.names = FALSE,
  quote = FALSE
)

message("Wrote final full-centered 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] en_CA/en_CA/en_CA/C/en_CA/en_CA

time zone: America/Chicago
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 whisker_0.4.1  
[25] stringr_1.6.0   compiler_4.5.1  fs_1.6.6        Rcpp_1.1.1-1.1 
[29] pkgconfig_2.0.3 later_1.4.4     digest_0.6.39   R6_2.6.1       
[33] pillar_1.11.1   magrittr_2.0.5  bslib_0.9.0     tools_4.5.1    
[37] cachem_1.1.0