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File Version Author Date Message
Rmd 4307b28 Ziang Zhang 2026-09-10 New main Figure 4, and fold the cluster heatmap into Extended Data Figure 2
html a7a481f Ziang Zhang 2026-09-09 Build site: the rebuilt Figure 1d and the Extended Data renumbering
Rmd c233cd8 Ziang Zhang 2026-09-09 Extended Data reorganisation: split the tissue/cluster figure, renumber 3-8
html 1e88d7e Ziang Zhang 2026-09-04 Build site: published captions and titles across all 24 pages
Rmd 0267e5b Ziang Zhang 2026-09-04 Captions from the published manuscript; trim editor notes off the page code
html 19c977f Ziang Zhang 2026-09-02 Build site: Extended Data 5-7 renumbered, Figure S5 page rebuilt
html eeca07b Ziang Zhang 2026-08-05 Keep pre-refactor provenance in panel comments off the published pages
Rmd 5651d0e Ziang Zhang 2026-08-05 Extended Data tables: reorder to six, rebuild Table 1, drop internal notes
html cbcec52 Ziang Zhang 2026-07-30 Build site: Extended Data Figure naming
Rmd 66aa029 Ziang Zhang 2026-07-30 Name the Extended Data figures as published on the site
html ac650a0 Ziang Zhang 2026-07-30 Build site: Figure S5 (ex-S6a) and Figure S6 as a-f
html 62e6e83 Ziang Zhang 2026-07-29 Build site: Figure 4 without the TF-GP network
Rmd 77f3337 Ziang Zhang 2026-07-29 Drop Figure 4’s TF-GP network; re-letter 4d/4e to 4c/4d
html ae21d37 Ziang Zhang 2026-07-28 Build site: republish after the reorder commits
html d538aa2 Ziang Zhang 2026-07-28 Build site: reordered Figures 6 / S6 / S3 and the new Figure 7b page
html 029b0ae Ziang Zhang 2026-07-28 Build site.
Rmd 0f5b5da Ziang Zhang 2026-07-28 Align all figure captions with captions_20260728_final.docx
html 3fc3789 Ziang Zhang 2026-07-27 Republish all 24 pages
html 1390a03 Ziang Zhang 2026-07-27 Republish all 24 pages
html adaef21 Ziang Zhang 2026-07-27 Build site: panel fixes and PDF-derived assets
html 5b19858 Ziang Zhang 2026-07-27 Build site.
Rmd 91ee059 Ziang Zhang 2026-07-26 Select each panel’s code block by name, not by line number
html 91ee059 Ziang Zhang 2026-07-26 Select each panel’s code block by name, not by line number
Rmd 620afca Ziang Zhang 2026-07-24 Align Fig3/4/5 section titles with current numbers; reletter Fig4 to a-e
html 620afca Ziang Zhang 2026-07-24 Align Fig3/4/5 section titles with current numbers; reletter Fig4 to a-e
Rmd 2873ad2 Ziang Zhang 2026-07-16 Unify Fig 3/4/5 panel filenames with new figure numbers
html 2873ad2 Ziang Zhang 2026-07-16 Unify Fig 3/4/5 panel filenames with new figure numbers
Rmd b197499 Ziang Zhang 2026-07-16 Restructure main figures (renumber + split Figure 1)
html b197499 Ziang Zhang 2026-07-16 Restructure main figures (renumber + split Figure 1)
html 6d1c6c0 Ziang Zhang 2026-07-04 Fix TableS1 to use non-thymocyte (not healthy-restricted) AUC, matching published Table S1
html 92021bf Ziang Zhang 2026-07-02 Build site.
html 827c89b Ziang Zhang 2026-07-02 Build site.
Rmd 8ac7f9f Ziang Zhang 2026-07-02 Add data provenance notes to each script; remove conversational
Rmd f9db962 Ziang Zhang 2026-07-02 Simplify layout: drop old code/script folders, rename
html c6e5086 Ziang Zhang 2026-07-02 Build site.
html 5a79883 Ziang Zhang 2026-07-02 Build site.
Rmd 2b0e445 Ziang Zhang 2026-07-02 Fix GitHub source links to point at the new
html cf1d0ac Ziang Zhang 2026-07-02 Build site.
Rmd 06b2461 Ziang Zhang 2026-07-02 Initial commit: immgenT-GP-analysis
html 06b2461 Ziang Zhang 2026-07-02 Initial commit: immgenT-GP-analysis

All panels are produced by script/Figure4.R, which shares its GP selection and cells with Extended Data Figure 4 via code/R/structure_plot_panels.R, and its heatmap rendering with Extended Data Figure 2d via code/R/centered_mean_heatmap.R. The code below is shown for reference (not re-executed on this page); the images are its pre-rendered output.

Setup

Data loading, shared across all panels below.

suppressPackageStartupMessages({
  library(ggplot2)
  library(ggrepel)
  library(ggrastr)
  library(cowplot)
  library(dplyr)
  library(fastTopics)      # structure_plot()
  library(ComplexHeatmap)
  library(circlize)
  library(grid)
  library(ZemmourLib)      # immgent_colors
})

if (!file.exists("code/R/structure_plot_panels.R")) {
  stop("Run this script from the immgenT-GP-analysis repository root.")
}
source("code/R/structure_plot_panels.R")   # Extended Data Figure 4's rows, GP rule, palette
source("code/R/centered_mean_heatmap.R")   # Extended Data Figure 2d's heatmap rendering

data_path   <- "data/"
figure_path <- "figures/final-selected/Figure 4/"
record_path <- "output/Figure4/"
dir.create(figure_path, recursive = TRUE, showWarnings = FALSE)
dir.create(record_path, recursive = TRUE, showWarnings = FALSE)

# Figure scripts here have been seen to exit 0 having written nothing, so every
# PDF is checked against this timestamp at the end rather than against the exit
# code (script/README.md, "A re-run can silently not write").
run_started_at <- Sys.time()

# ============================================================
# Load data
# ============================================================
seurat_meta <- readRDS(paste0(
  data_path, "igt1_96_withtotalvi20260206_clean_ADTonly.Rds"
))@meta.data
L_pm_filtered <- readRDS(paste0(data_path, "L_pm_filtered.rds"))
seurat_meta_filtered <- seurat_meta[rownames(L_pm_filtered), ]
colnames(L_pm_filtered) <- gsub("^K", "GP", colnames(L_pm_filtered))
if (!all(grepl("^GP[0-9]+$", colnames(L_pm_filtered)))) {
  stop("The loading matrix does not carry K##/GP## column names.")
}

level_1_AUC_list <- readRDS(paste0(
  data_path, "level_1_AUC_list_figure_no_thymocytes_healthy.rds"
))
level_2_AUC_list <- readRDS(paste0(
  data_path, "level_2_AUC_list_figure_no_thymocytes_healthy.rds"
))
colnames(level_1_AUC_list$auc) <- gsub("^K", "GP", colnames(level_1_AUC_list$auc))
colnames(level_2_AUC_list$auc) <- gsub("^K", "GP", colnames(level_2_AUC_list$auc))

# Both AUC matrices index GPs by names taken from the loading matrix, so they
# have to be talking about the same GPs in the same order.
if (!identical(colnames(level_1_AUC_list$auc), colnames(L_pm_filtered)) ||
      !identical(colnames(level_2_AUC_list$auc), colnames(L_pm_filtered))) {
  stop("The AUC matrices and L_pm_filtered do not carry the same GP columns in the same order.")
}

level1_all <- seurat_meta_filtered$annotation_level1
level2_all <- seurat_meta_filtered$annotation_level2
healthy_non_thymocyte <- which(
  seurat_meta_filtered$condition_broad == "healthy" & level1_all != "thymocyte"
)
meta_reference <- seurat_meta_filtered[healthy_non_thymocyte, , drop = FALSE]
L_healthy <- L_pm_filtered[healthy_non_thymocyte, , drop = FALSE]
overall_mean <- colMeans(L_healthy, na.rm = TRUE)

message(sprintf(
  "%d healthy non-thymocyte cells, %d GPs, %d level-2 clusters",
  nrow(L_healthy), ncol(L_healthy), dplyr::n_distinct(meta_reference$annotation_level2)
))

(a) Lineage vs. cluster prediction

# ============================================================
# 4a: Max AUC level-1 vs max AUC level-2
# ============================================================
# Figure 6a and 6d's construction, with their category-count filters kept as
# they are there: level-1 lineages need 1000 cells, level-2 clusters 100.
level_1_small <- table(meta_reference$annotation_level1)
level_1_small <- names(level_1_small[level_1_small < 1000])
level_1_AUC <- level_1_AUC_list$auc[
  !rownames(level_1_AUC_list$auc) %in% level_1_small, , drop = FALSE
]

level_2_small <- table(meta_reference$annotation_level2)
level_2_small <- names(level_2_small[level_2_small < 100])
level_2_AUC <- level_2_AUC_list$auc[
  !rownames(level_2_AUC_list$auc) %in% level_2_small, , drop = FALSE
]

# A high AUC only means "GP predicts this category" when the category's mean
# loading is above the overall mean; the other direction is a GP the category
# lacks. Same masking as Figure 6.
category_mean <- function(labels, categories) {
  t(vapply(categories, function(cat) {
    colMeans(L_healthy[labels == cat, , drop = FALSE], na.rm = TRUE)
  }, numeric(ncol(L_healthy))))
}
level_1_positive <- sweep(
  category_mean(meta_reference$annotation_level1, rownames(level_1_AUC)),
  2, overall_mean, "-"
) > 0
level_2_positive <- sweep(
  category_mean(meta_reference$annotation_level2, rownames(level_2_AUC)),
  2, overall_mean, "-"
) > 0

masked_max <- function(auc, positive) {
  auc[!positive] <- NA
  list(
    value = apply(auc, 2, max, na.rm = TRUE),
    name = apply(auc, 2, function(x) rownames(auc)[which.max(x)])
  )
}
l1_max <- masked_max(level_1_AUC, level_1_positive)
l2_max <- masked_max(level_2_AUC, level_2_positive)

df_a <- data.frame(
  Factor = colnames(level_1_AUC),
  annotation_Level1 = l1_max$name,
  annotation_Level2 = l2_max$name[colnames(level_1_AUC)],
  Max_AUC_Level1 = l1_max$value,
  Max_AUC_Level2 = l2_max$value[colnames(level_1_AUC)],
  stringsAsFactors = FALSE
) |>
  dplyr::mutate(residual = Max_AUC_Level2 - Max_AUC_Level1)

# Figure 6a's highlight rule verbatim: AUC > 0.9 on either axis, coloured by
# which of the two maxima is the larger. Only the labelling differs -- GP names,
# not each point's top categories.
highlighted <- df_a |>
  dplyr::filter(is.finite(residual), Max_AUC_Level1 > 0.9 | Max_AUC_Level2 > 0.9) |>
  dplyr::pull(Factor)

label_above <- df_a |>
  dplyr::filter(Factor %in% highlighted, residual > 0) |>
  dplyr::mutate(
    label_text = Factor
  )
label_below <- df_a |>
  dplyr::filter(Factor %in% highlighted, residual <= 0) |>
  dplyr::mutate(
    label_text = Factor
  )
message(sprintf(
  "a: %d of %d GPs highlighted (%d above the diagonal, %d on or below)",
  length(highlighted), nrow(df_a), nrow(label_above), nrow(label_below)
))

axis_limits <- function(x, pad = 0.04) {
  x <- x[is.finite(x)]
  c(min(x) - pad, max(x) + pad)
}

p_4a <- ggplot(df_a, aes(Max_AUC_Level1, Max_AUC_Level2)) +
  geom_point(alpha = 0.3, size = 1.8) +
  geom_point(data = label_above, color = "#1f78b4", alpha = 0.8, size = 1.8) +
  geom_point(data = label_below, color = "#e31a1c", alpha = 0.8, size = 1.8) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black") +
  coord_cartesian(
    xlim = axis_limits(df_a$Max_AUC_Level1),
    ylim = axis_limits(df_a$Max_AUC_Level2),
    expand = FALSE, clip = "off"
  ) +
  labs(
    x = "Max AUC (Level-1)", y = "Max AUC (Level-2)",
    title = "Max AUC: Level-1 vs Level-2"
  ) +
  theme_minimal(base_size = 13) +
  theme(plot.margin = margin(10, 40, 10, 40)) +
  geom_text_repel(
    seed = 42, data = label_above, aes(label = label_text), color = "#1f78b4",
    size = 2.5, lineheight = 0.85, direction = "y", nudge_x = -0.035,
    segment.color = "#1f78b4",
    arrow = arrow(length = unit(0.008, "npc"), type = "closed", angle = 20),
    force = 3, force_pull = 0.1, box.padding = 0.4, point.padding = 0.15,
    max.time = 10, max.iter = 2e4, max.overlaps = 20,
    min.segment.length = 0.01, segment.alpha = 0.7
  ) +
  geom_text_repel(
    seed = 42, data = label_below, aes(label = label_text), color = "#e31a1c",
    size = 2.5, lineheight = 0.85, direction = "y", nudge_x = 0.035,
    segment.color = "#e31a1c",
    arrow = arrow(length = unit(0.008, "npc"), type = "closed", angle = 20),
    force = 3, force_pull = 0.1, box.padding = 0.4, point.padding = 0.15,
    max.time = 10, max.iter = 2e4, max.overlaps = 20,
    min.segment.length = 0.01, segment.alpha = 0.7
  )
ggsave(paste0(figure_path, "4a.pdf"), p_4a, width = 8, height = 8, dpi = 300)

write.csv(
  df_a[order(-df_a$Max_AUC_Level2), c(
    "Factor", "Max_AUC_Level1", "annotation_Level1",
    "Max_AUC_Level2", "annotation_Level2", "residual"
  )],
  file.path(record_path, "4a_max_auc_level1_level2.csv"),
  row.names = FALSE
)

Version Author Date
7102598 Ziang Zhang 2026-07-27
6c1a613 Ziang Zhang 2026-07-27
bf7afcf Ziang Zhang 2026-07-27
ea3ecc2 Ziang Zhang 2026-07-27
620afca Ziang Zhang 2026-07-24
b197499 Ziang Zhang 2026-07-16
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. 4a. Scatterplot showing each GP’s prediction (AUC) of level 1 lineage (x-axis) versus level 2 cluster (y-axis), in healthy non-thymocyte cells. Each point is one GP; the dashed line is the identity. GPs reaching AUC > 0.9 on either axis are colored and labelled: blue where the cluster AUC is the larger of the two, red where the lineage AUC is. Most GPs sit above the identity line, predicting a cluster better than any whole lineage.

(b) The cluster-associated GPs across clusters

# ============================================================
# 4b: the same GPs as a heatmap, level-2 columns grouped by level 1
# ============================================================
cells_drawn <- sort(unlist(lineage_cells_drawn, use.names = FALSE))
labels_drawn <- droplevels(factor(level2_all[cells_drawn]))
heat_means <- mean_loading_by_group(
  L_pm_filtered[cells_drawn, gp_union, drop = FALSE], labels_drawn
)
heat_raw <- heat_means$matrix          # GPs x clusters
heat_centered <- center_by_gp_mean(heat_raw)

# Figure 1's level-1 order, minus DP and thymocytes, which these rows exclude.
level1_order <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN")
level2_group_level1 <- level2_to_level1_map(
  meta_reference, colnames(heat_raw), level1_order
)
heat_order <- dominant_group_order(
  heat_raw,
  level2_column_order(colnames(heat_raw), level2_group_level1, level1_order)
)

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

centered_color_limit <- 0.2
render_centered_heatmap(
  heat_centered,
  level2_palette,
  "cluster (annotation_level2)",
  paste0(figure_path, "4b.pdf"),
  heat_order$row_order,
  heat_order$column_order,
  centered_color_limit,
  sprintf(
    paste0(
      "%d GPs with AUC > %.1f in some cluster; level2 columns: level1 order ",
      "(%s); alphabetical within level1; GP rows: dominant-cluster blocks"
    ),
    nrow(heat_centered), structure_plot_auc_threshold,
    paste(level1_order, collapse = ", ")
  ),
  group_level1 = level2_group_level1,
  level1_palette = level1_palette
)

write.csv(
  data.frame(gp = rownames(heat_centered), heat_centered, check.names = FALSE),
  file.path(record_path, "4b_row_centered_mean_loading.csv"), row.names = FALSE
)
write.csv(
  data.frame(
    cluster = heat_means$counts$group,
    level1 = unname(level2_group_level1[heat_means$counts$group]),
    n_cells = heat_means$counts$n_cells
  ),
  file.path(record_path, "4b_column_cells.csv"), row.names = FALSE
)

Version Author Date
7102598 Ziang Zhang 2026-07-27
6c1a613 Ziang Zhang 2026-07-27
bf7afcf Ziang Zhang 2026-07-27
ea3ecc2 Ziang Zhang 2026-07-27
620afca Ziang Zhang 2026-07-24
b197499 Ziang Zhang 2026-07-16
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. 4b. Heatmap of mean GP activity per level 2 cluster, for the 69 GPs that reach AUC > 0.9 in at least one cluster. Each row is centered on that GP’s mean across the clusters shown (blue, below; red, above; saturating at 0.2). Columns are the clusters with at least 100 healthy non-thymocyte cells, grouped by lineage (top bar) and alphabetical within each lineage; rows are grouped by the cluster in which the GP is most active.

(c) Mean GP activity of the Treg clusters

# ============================================================
# 4c: one stacked bar of mean loadings per level-2 cluster, the Treg row
# ============================================================
# The Treg row of Extended Data Figure 4 with one bar per cluster, holding that
# cluster's mean loading per GP instead of one bar per cell -- the average of
# that row's bars. Same GPs (the 11 with AUC > 0.9 in some Treg cluster), same
# per-row palette, same cells, minus Treg.wM.
#
# Bars are raw mean loadings, not rescaled to a common height: that is what
# makes them the average of those bars, since structure_plot() does not
# renormalize a subset of topics either. A tall bar means the cluster carries
# more total activity over the row's GPs, not just a different mix.
#
#
# Unlike 4d and Extended Data Figure 4 this panel is not drawn at the structure
# plot's 16 x 3 in: a chart of seven bars does not need a structure plot's
# width, and Ziang asked for tall and narrow.
bar_figure_width <- 5.5  # inches
bar_figure_height <- 7

# Mean loading per cluster over the row's GPs, plus the record of every segment.
bar_means <- function(lineage) {
  gps_lineage <- panel_gps[[lineage]]
  means <- mean_loading_by_cluster(lineage_cells_bars[[lineage]], gps_lineage)
  bar_matrix <- means$matrix
  totals <- rowSums(bar_matrix)
  if (any(!is.finite(totals)) || any(totals <= 0)) {
    stop(sprintf("row %s: a cluster has no loading at all.", lineage))
  }

  # This row's colours, assigned from the top of the palette without reference
  # to any other row -- the same per-row rule Extended Data Figure 4 uses, so
  # colour means different GPs in different rows.
  colors_lineage <- structure_plot_row_colors(gps_lineage)
  if (!identical(names(colors_lineage), colnames(bar_matrix))) {
    stop(sprintf("row %s: palette order does not match its GP columns.", lineage))
  }

  long <- data.frame(
    cluster = factor(
      rep(rownames(bar_matrix), times = ncol(bar_matrix)),
      levels = rownames(bar_matrix)
    ),
    gp = factor(
      rep(colnames(bar_matrix), each = nrow(bar_matrix)),
      levels = colnames(bar_matrix)
    ),
    value = as.vector(bar_matrix),
    stringsAsFactors = FALSE
  )

  record <- data.frame(
    panel = structure_plot_panels[[lineage]],
    lineage = lineage,
    drawn_in_4c = lineage == bar_lineage,
    cluster = as.character(long$cluster),
    n_cells = means$counts[match(long$cluster, rownames(bar_matrix))],
    gp = as.character(long$gp),
    color = unname(colors_lineage[as.character(long$gp)]),
    mean_loading = long$value,
    # The same segment as a share of its cluster's total -- the normalized view's
    # number, kept so that variant can be redrawn from the record alone.
    proportion = long$value / totals[as.character(long$cluster)],
    stringsAsFactors = FALSE
  )

  list(long = long, colors = colors_lineage, record = record, gps = gps_lineage)
}

bar_lineage <- "Treg"
bar_data <- lapply(names(structure_plot_panels), bar_means)
names(bar_data) <- names(structure_plot_panels)

drawn <- bar_data[[bar_lineage]]
p_4c <- ggplot(drawn$long, aes(cluster, value, fill = gp)) +
  geom_col(width = 0.85) +
  scale_fill_manual(values = drawn$colors) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.02))) +
  labs(
    x = "", y = "mean loading", fill = "",
    title = sprintf(
      "%s (%d GPs, AUC > %.1f)", bar_lineage, length(drawn$gps),
      structure_plot_auc_threshold
    )
  ) +
  guides(fill = guide_legend(ncol = 1)) +
  cowplot::theme_cowplot(9) +
  theme(
    plot.title = element_text(size = 11, face = "bold"),
    axis.text.x = element_text(size = 7, angle = 45, hjust = 1),
    axis.text.y = element_text(size = 9),
    axis.title = element_text(size = 10, face = "bold"),
    axis.line = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "right",
    legend.key.size = unit(0.3, "cm"),
    legend.text = element_text(size = 7)
  )
ggsave(
  paste0(figure_path, "4c.pdf"), p_4c,
  width = bar_figure_width, height = bar_figure_height, dpi = 300
)

# Every bar segment of all seven rows, raw and as a share of its cluster's
# total; drawn_in_4c marks the row this panel shows.
bar_record <- do.call(rbind, lapply(bar_data, `[[`, "record"))
write.csv(bar_record, file.path(record_path, "4c_mean_loading_by_cluster.csv"), row.names = FALSE)

Version Author Date
62e6e83 Ziang Zhang 2026-07-29
7102598 Ziang Zhang 2026-07-27
6c1a613 Ziang Zhang 2026-07-27
bf7afcf Ziang Zhang 2026-07-27
ea3ecc2 Ziang Zhang 2026-07-27
620afca Ziang Zhang 2026-07-24
2873ad2 Ziang Zhang 2026-07-16
b197499 Ziang Zhang 2026-07-16
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. 4c. Mean GP activity of each Treg cluster, for the 11 GPs reaching AUC > 0.9 in at least one Treg cluster. Each bar is one cluster and is stacked by GP, so its height is that cluster’s total mean loading over these GPs. Clusters with fewer than 100 healthy non-thymocyte cells, and the wM cluster, are not shown.

(d) The same GPs and cells, cell by cell

# ============================================================
# 4d: Extended Data Figure 4's Treg row on its own
# ============================================================
# FigureS4.R's loop body for one lineage, unchanged including its seeds and its
# 2000-cell cap, so this is that row and not a redrawing of it.
d_lineage <- "Treg"
gps_d <- panel_gps[[d_lineage]]
cells_d <- healthy_non_thymocyte[level1_all[healthy_non_thymocyte] == d_lineage]
cluster_size_d <- table(droplevels(factor(level2_all[cells_d])))
small_d <- names(cluster_size_d)[cluster_size_d < structure_plot_min_cluster_cells]
cells_d <- cells_d[!level2_all[cells_d] %in% small_d]
cells_d <- drop_wM(cells_d)   # as in 4c; Treg.wM's 654 cells are not drawn

set.seed(1234)
keep_d <- unlist(lapply(
  split(seq_along(cells_d), level2_all[cells_d]),
  function(idx) {
    if (length(idx) > structure_plot_max_cells_per_cluster) {
      sample(idx, structure_plot_max_cells_per_cluster)
    } else {
      idx
    }
  }
))
cells_d <- cells_d[keep_d]

fit_d <- L_pm_filtered[cells_d, gps_d, drop = FALSE]
colors_d <- structure_plot_row_colors(gps_d)
if (!identical(names(colors_d), colnames(fit_d))) {
  stop("4d: palette order does not match its GP columns.")
}

set.seed(1234)
p_4d <- structure_plot(
  fit_d, topics = gps_d, gap = 40, n = 10000, colors = colors_d,
  grouping = factor(level2_all[cells_d]),
  ggplot_call = rasterized_structure_plot_call
) +
  labs(
    y = "membership", color = "", fill = "",
    title = sprintf(
      "%s (%d GPs, AUC > %.1f)", d_lineage, length(gps_d), structure_plot_auc_threshold
    )
  ) +
  guides(fill = guide_legend(ncol = 2), color = guide_legend(ncol = 2)) +
  theme(
    plot.title = element_text(size = 11, face = "bold"),
    axis.text.x = element_text(size = 6, angle = 45, hjust = 1),
    axis.text.y = element_text(size = 9),
    axis.title = element_text(size = 10, face = "bold"),
    legend.position = "right",
    legend.key.size = unit(0.25, "cm"),
    legend.text = element_text(size = 5),
    legend.spacing.y = unit(0.02, "cm")
  )
ggsave(
  paste0(figure_path, "4d.pdf"), p_4d,
  width = structure_plot_width, height = structure_plot_row_height,
  dpi = 300, limitsize = FALSE
)

Version Author Date
62e6e83 Ziang Zhang 2026-07-29
7102598 Ziang Zhang 2026-07-27
6c1a613 Ziang Zhang 2026-07-27
bf7afcf Ziang Zhang 2026-07-27
ea3ecc2 Ziang Zhang 2026-07-27
620afca Ziang Zhang 2026-07-24
2873ad2 Ziang Zhang 2026-07-16
b197499 Ziang Zhang 2026-07-16
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. 4d. Structure plot of the same GPs in the same cells at single-cell resolution: each bar is one cell, colored by its GP membership and grouped by cluster. Colors match (c). Panel (c) is the per-cluster average of these bars.


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_1.8.9   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