Figure 6: Tissue-associated gene programs reflect both tissue adaptation and T cell subset composition
Ziang Zhang
Last updated: 2026-09-10
Checks: 7 0
Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7fc9cfa | Ziang Zhang | 2026-09-10 | Figure 6 rebuilt from the fig_n5 draft: two mean-loading heatmaps lead |
| html | 60125af | Ziang Zhang | 2026-09-10 | Build site: Figure 4b on the shared column colours |
| html | 88ee847 | Ziang Zhang | 2026-09-10 | Build site: Figure 4b without the miniverse clusters |
| html | 5874416 | Ziang Zhang | 2026-09-10 | Build site: main Figure 4 inserted, Extended Data back to 1-7 |
| Rmd | 4307b28 | Ziang Zhang | 2026-09-10 | New main Figure 4, and fold the cluster heatmap into Extended Data Figure 2 |
| html | bf4f612 | Ziang Zhang | 2026-09-09 | Build site: Extended Data back to 1-8 |
| Rmd | 6f01135 | Ziang Zhang | 2026-09-09 | Pull the tissue figure back out of Extended Data; ED is 1-8 again |
| 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 | 796eeba | Ziang Zhang | 2026-09-04 | Build site: the three corrected captions |
| Rmd | 2344a4a | Ziang Zhang | 2026-09-04 | Three captions corrected where the published text and the panel disagree |
| 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 |
| Rmd | 1e5721a | Ziang Zhang | 2026-09-02 | Extended Data 5-7 renumbered, and Figure S5 assembled as one stacked figure |
| html | ae03072 | Ziang Zhang | 2026-08-19 | Build site: six pages rebuilt after the prose cleanup |
| Rmd | adc2327 | Ziang Zhang | 2026-08-19 | Site prose: finish taking internal notes off the pages |
| 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 |
| Rmd | c9b020f | Ziang Zhang | 2026-07-30 | Split Figure S6’s protein-program heatmap out as Figure S5 |
| html | 732ac8d | Ziang Zhang | 2026-07-29 | Build site: caption alignment with captions_20260729_final.docx |
| Rmd | 8d73953 | Ziang Zhang | 2026-07-28 | Align captions with captions_20260729_final.docx |
| 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 |
| Rmd | 4c07670 | Ziang Zhang | 2026-07-28 | Reorder Figures 6, S6 and S3; make the ex-S5 figure Figure 7b |
| 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 | 58eab27 | Ziang Zhang | 2026-07-28 | Build site: Fig. 6i-k caption on the curated CD69 GP subset |
| Rmd | 589a0ed | Ziang Zhang | 2026-07-28 | Fig. 6i-k: say the 10 CD69 GPs are curated, not the top 10 |
| 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 | ffe285c | Ziang Zhang | 2026-07-27 | Reorganize figures/ and untrack local-only exploration notes |
| 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 | 06552b8 | Ziang Zhang | 2026-07-14 | Update Figure 6b heatmap layout |
| html | 06552b8 | Ziang Zhang | 2026-07-14 | Update Figure 6b heatmap layout |
| html | 92021bf | Ziang Zhang | 2026-07-02 | Build site. |
| Rmd | db7a5cd | Ziang Zhang | 2026-07-02 | Recover 3 data-provenance gaps into pipeline scripts |
| 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/Figure6.R.
The code below is shown for reference (not re-executed on this page);
the images are its pre-rendered output.
Setup
Data loading and the tissue-associated GP set, shared across the panels below.
# Figure 6. GPs and tissue.
#
# Panels produced:
# 6a Row-centered mean GP activity across the 18 tissues, for the 32
# tissue-associated GPs, tissues in organ_simplified factor order.
# 6b The same 32 GPs and row order, across the 8 lineages.
# 6c GP37+ rate by lineage, mammary gland vs. the same lineage elsewhere.
# 6d Organ AUC vs Level-2 (fine-grained sub-lineage/cluster) AUC, with the
# 7 organ-specific GPs (red) and a contrasting cluster-specific set
# (blue) highlighted.
# 6e Alluvial diagram: organ of origin -> GP -> Level-2 cell type, for
# GP+ cells of the 7 organ-specific GPs.
#
# Required inputs (data/) -- see code/README.md's "Data provenance" table
# for the full picture:
# L_pm_filtered.rds [code/pipeline/01b_filter_cells.R]
# igt1_96_..._ADTonly.Rds [primary input Seurat object]
# level_2_AUC_list_figure_no_thymocytes_healthy.rds,
# organ_simplified_AUC_list_figure_no_thymocytes_healthy.rds
# [code/pipeline/02_compute_auc.R]
library(ggplot2)
library(ggrepel)
library(patchwork)
library(dplyr)
library(tidyr)
library(purrr)
library(tibble)
library(Matrix)
library(viridis)
library(cowplot)
library(ggalluvial)
suppressPackageStartupMessages({
library(ComplexHeatmap)
library(circlize)
library(grid)
library(ZemmourLib)
})
data_path <- "data/"
figure_path <- "figures/final-selected/Figure 6/"
run_started_at <- Sys.time()
# Group means, row centering, dominant-group ordering and the heatmap call for
# 6a/6b; shared with Extended Data Figure 2d, which draws the cluster version.
source("code/R/centered_mean_heatmap.R")
# ============================================================
# Load data (healthy, non-thymocyte reference)
# ============================================================
level_2_AUC_list <- readRDS(paste0(
data_path, "level_2_AUC_list_figure_no_thymocytes_healthy.rds"
))
organ_AUC_list <- readRDS(paste0(
data_path, "organ_simplified_AUC_list_figure_no_thymocytes_healthy.rds"
))
# Metadata is read from the Seurat object directly.
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), ]
# Rename K## to GP## for display consistency
colnames(L_pm_filtered) <- gsub("^K", "GP", colnames(L_pm_filtered))
colnames(level_2_AUC_list$auc) <- gsub("^K", "GP", colnames(level_2_AUC_list$auc))
colnames(level_2_AUC_list$threshold) <- gsub("^K", "GP", colnames(level_2_AUC_list$threshold))
colnames(organ_AUC_list$auc) <- gsub("^K", "GP", colnames(organ_AUC_list$auc))
colnames(organ_AUC_list$threshold) <- gsub("^K", "GP", colnames(organ_AUC_list$threshold))
# Restrict reference to healthy, non-thymocyte cells
seurat_meta_filtered_no_thymocytes_healthy <- seurat_meta_filtered %>%
filter(annotation_level1 != "thymocyte", condition_broad == "healthy")
# The 7 organ-specific GPs highlighted throughout this figure (caption 6d/6e)
gps_of_interest <- c("GP3", "GP6", "GP11", "GP26", "GP29", "GP37", "GP177")
# Column order for 6a/6b: levels(so_orig$organ_simplified) and the Figure 1
# lineage order. Organ levels with no healthy non-thymocyte cells drop out.
organ_display_levels <- c(
"blood", "spleen", "LN", "SLO", "bone marrow", "thymus",
"lung", "liver", "peritoneal cavity",
"colon epi", "small intestine epi", "colon LP", "small intestine LP",
"skin", "mammary gland", "uterus", "placenta", "prostate",
"submandibular gland", "kidney", "pancreas", "CNS", "synovial fluid"
)
level1_display_levels <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN", "DP")
organ_color_limit <- 0.2
level1_color_limit <- 0.1
# Labels a highlighted point with its top categories above `threshold` AUC.
top_cats_label <- function(factor_name, auc_matrix, positive_mask, threshold = 0.85, n = 3) {
vals <- auc_matrix[, factor_name]
vals <- vals[positive_mask[, factor_name]]
vals <- sort(vals[vals > threshold], decreasing = TRUE)
cats <- names(vals)[seq_len(min(n, length(vals)))]
if (length(cats) == 0) return(factor_name)
paste0(factor_name, ":\n", paste(cats, collapse = "\n"))
}
# ============================================================
# Shared AUC setup: the organ AUC matrix and the healthy reference means
# ============================================================
# Organs with fewer than 100 healthy non-thymocyte cells are dropped, matching
# the filter every AUC panel in this figure uses.
organ_AUC <- organ_AUC_list$auc
organ_small_count <- table(seurat_meta_filtered_no_thymocytes_healthy$organ_simplified)
organ_small_count <- names(organ_small_count[organ_small_count < 100])
organ_AUC <- organ_AUC[!rownames(organ_AUC) %in% organ_small_count, ]
healthy_cells <- rownames(seurat_meta_filtered_no_thymocytes_healthy)
L_healthy <- L_pm_filtered[healthy_cells, ]
overall_mean <- colMeans(L_healthy, na.rm = TRUE)
stopifnot(nrow(L_healthy) == nrow(seurat_meta_filtered_no_thymocytes_healthy), !anyNA(L_healthy))The 32 tissue-associated GPs
Panels (a) and (b) show the union of the two criteria by which a GP is called tissue-associated: its raw mean loading reaches 0.1 in at least one of the 18 tissues (31 GPs), or its one-vs-rest tissue AUC exceeds 0.9 under the positivity mask (7 GPs, the organ-specific set highlighted in (d) and (e)). Six GPs satisfy both, so 32 are shown.
# ============================================================
# 6a/6b selection: the 32 tissue-associated GPs
# ============================================================
# The union of the two criteria the manuscript uses for "tissue-associated":
#
# A raw (uncentered) mean loading >= 0.1 in at least one of the 18 tissues
# -- 31 GPs, the "programs active across tissues";
# B one-vs-rest tissue AUC > 0.9 under the positivity mask (mean loading in
# the tissue above the GP's overall mean) -- 7 GPs, the organ-specific set
# this figure highlights in 6d and 6e.
#
# Six GPs satisfy both, so 32 are shown. GP37 is the only member of B alone:
# its mammary-gland AUC is 0.9918, but it is active in 2.5% of mammary gland
# cells, which dilutes its mean loading there to 0.011 -- an order of magnitude
# under A's cutoff. Its row is therefore near-white in 6a and 6b by
# construction, which is why it is parked on the last row rather than left as a
# hole inside the mammary gland block; 6c is the panel that shows what it does.
#
# Rule B is re-derived rather than reusing gps_of_interest, and then asserted
# equal to it, so the selection and the highlighting cannot drift apart.
raw_mean_cutoff <- 0.1
auc_cutoff <- 0.9
last_row_gp <- "GP37"
organ_raw <- mean_loading_by_group(L_healthy, seurat_meta_filtered_no_thymocytes_healthy$organ_simplified)$matrix
level1_raw <- mean_loading_by_group(L_healthy, seurat_meta_filtered_no_thymocytes_healthy$annotation_level1)$matrix
tissue_active <- rowSums(organ_raw >= raw_mean_cutoff) > 0L
organ_AUC_positive <- sweep(
t(sapply(rownames(organ_AUC), function(cat) {
colMeans(L_healthy[seurat_meta_filtered_no_thymocytes_healthy$organ_simplified == cat, , drop = FALSE], na.rm = TRUE)
})),
2, overall_mean, "-"
) > 0
tissue_specific <- rownames(organ_raw) %in%
colnames(organ_AUC)[colSums((organ_AUC > auc_cutoff) & organ_AUC_positive) > 0L]
selected <- tissue_active | tissue_specific
tissue_associated_gps <- rownames(organ_raw)[selected]
expected_gps <- paste0("GP", c(
1, 3, 4, 6, 8, 9, 11, 22, 23, 25, 26, 29, 30, 32, 35, 37, 41, 43, 49, 51, 58,
62, 63, 72, 80, 93, 100, 166, 170, 171, 174, 177
))
stopifnot(
setequal(rownames(organ_raw)[tissue_specific], gps_of_interest),
sum(tissue_active) == 31L,
sum(tissue_specific) == 7L,
identical(tissue_associated_gps, expected_gps),
last_row_gp %in% expected_gps
)
organ_selected_raw <- organ_raw[selected, , drop = FALSE]
level1_selected_raw <- level1_raw[selected, , drop = FALSE]
organ_centered <- center_by_gp_mean(organ_selected_raw)
level1_centered <- center_by_gp_mean(level1_selected_raw)
# Columns follow levels(so_orig$organ_simplified) and levels(annotation_level1)
# rather than a dominant-group ordering. Five organ levels (SLO, thymus,
# prostate, pancreas, synovial fluid) have no healthy non-thymocyte cells and
# drop out; the other 18 keep their relative order. Rows are dominant-group
# blocks computed against that fixed column order, with GP37 appended last.
observed_organs <- intersect(organ_display_levels, colnames(organ_centered))
if (!setequal(observed_organs, colnames(organ_centered))) {
stop("A tissue in the reference is missing from organ_display_levels: ",
paste(setdiff(colnames(organ_centered), organ_display_levels), collapse = ", "))
}
organ_column_order <- match(observed_organs, colnames(organ_centered))
block_rows <- setdiff(rownames(organ_selected_raw), last_row_gp)
block_order <- dominant_group_order(
organ_selected_raw[block_rows, observed_organs, drop = FALSE],
fixed_column_order = seq_along(observed_organs)
)
organ_row_order <- match(
c(block_rows[block_order$row_order], last_row_gp), rownames(organ_centered)
)
level1_column_order <- match(level1_display_levels, colnames(level1_centered))
level1_row_order <- match(rownames(organ_centered)[organ_row_order], rownames(level1_centered))
stopifnot(
nrow(organ_centered) == 32L, ncol(organ_centered) == 18L, ncol(level1_centered) == 8L,
identical(rownames(organ_centered), rownames(level1_centered)),
!anyNA(organ_column_order), !anyNA(level1_column_order), !anyNA(organ_row_order),
identical(rownames(organ_centered)[organ_row_order[32]], last_row_gp),
max(abs(rowMeans(organ_centered))) < 1e-12,
max(abs(rowMeans(level1_centered))) < 1e-12
)(a) Tissue mean loading
# ============================================================
# 6a: the 32 tissue-associated GPs across the 18 tissues
# ============================================================
render_centered_heatmap(
organ_centered,
palette_for_groups(colnames(organ_centered), ZemmourLib::immgent_colors$organ_simplified, "organ_simplified"),
"tissue (organ_simplified)",
paste0(figure_path, "6a.pdf"),
organ_row_order, organ_column_order, organ_color_limit,
"32 tissue-associated GPs; organ_simplified order",
palette = heatmap_palettes$blue_red
)
Fig. 6a. Row-centered mean GP activity across 18 tissues, for the 32 tissue-associated GPs. For each GP, its mean loading across tissues is subtracted from every tissue mean, so the color says where a program is more or less active than its own average. Mean GP activity computed in samples at baseline. GP37 is shown on the last row: it is strongly mammary-gland-specific but active in few cells, so its mean loading is small at every tissue (see (c)).
(b) Lineage mean loading
# ============================================================
# 6b: the same 32 GPs across the 8 lineages
# ============================================================
# Green-white-purple at +/-0.1 rather than blue-white-red at +/-0.2: spreading a
# program over 8 lineages instead of 18 tissues gives correspondingly smaller
# deviations, and 6a's scale renders this panel almost blank. Purple is the
# positive end.
render_centered_heatmap(
level1_centered,
palette_for_groups(colnames(level1_centered), ZemmourLib::immgent_colors$level1, "annotation_level1"),
"lineage (annotation_level1)",
paste0(figure_path, "6b.pdf"),
level1_row_order, level1_column_order, level1_color_limit,
"the same 32 GPs, row order from (a)",
palette = heatmap_palettes$green_purple
)
| Version | Author | Date |
|---|---|---|
| 7fc9cfa | Ziang Zhang | 2026-09-10 |
| 5874416 | Ziang Zhang | 2026-09-10 |
| d538aa2 | Ziang Zhang | 2026-07-28 |
| 7102598 | Ziang Zhang | 2026-07-27 |
| 6c1a613 | Ziang Zhang | 2026-07-27 |
| bf7afcf | Ziang Zhang | 2026-07-27 |
| ea3ecc2 | Ziang Zhang | 2026-07-27 |
| 06552b8 | Ziang Zhang | 2026-07-14 |
| 78e3bba | Ziang Zhang | 2026-07-03 |
| 06b2461 | Ziang Zhang | 2026-07-02 |
Fig. 6b. The same 32 GPs across the 8 T cell lineages, in the row order of (a). Mean GP activity computed in samples at baseline. Note the different color scale: green-purple rather than blue-red, and -0.1 to 0.1 rather than -0.2 to 0.2, since spreading a program over 8 lineages instead of 18 tissues gives correspondingly smaller deviations.
(c) GP37+ rate in mammary gland vs. elsewhere
# ============================================================
# 6c: GP37+ rate by lineage, mammary gland vs. elsewhere
# ============================================================
plot_gp_threshold_group_activation_rate <- function(
gp, organ, threshold, loading_mat, organ_info, group_info,
group_label = "Level-2", base_size = 13, min_in_organ = 10,
group_colors = ZemmourLib::immgent_colors$level2, fallback_group_color = "grey60",
reference = c("not_in_group", "not_in_organ")
) {
reference <- match.arg(reference)
if (!gp %in% colnames(loading_mat)) stop(sprintf("GP '%s' not found in loading matrix.", gp))
if (!organ %in% organ_info) stop(sprintf("Organ '%s' not found in organ_info.", organ))
loading <- loading_mat[, gp]
keep <- !(is.na(loading) | is.na(organ_info) | is.na(group_info))
loading <- loading[keep]
organ_info <- organ_info[keep]
group_info <- as.character(group_info[keep])
in_organ <- organ_info == organ
positive <- loading > threshold
group_levels <- sort(unique(group_info))
rate_df <- data.frame(
group = group_levels,
n_in_organ = vapply(group_levels, function(l) sum(group_info == l & in_organ), integer(1)),
n_pos_in_organ = vapply(group_levels, function(l) sum(group_info == l & in_organ & positive), integer(1))
)
if (reference == "not_in_group") {
rate_df$n_ref <- vapply(group_levels, function(l) sum(group_info != l & in_organ), integer(1))
rate_df$n_pos_ref <- vapply(group_levels, function(l) sum(group_info != l & in_organ & positive), integer(1))
ref_label <- "Not in group (same organ)"
title_vs <- sprintf("%s vs. same-organ non-group", organ)
} else {
rate_df$n_ref <- vapply(group_levels, function(l) sum(group_info == l & !in_organ), integer(1))
rate_df$n_pos_ref <- vapply(group_levels, function(l) sum(group_info == l & !in_organ & positive), integer(1))
ref_label <- "Not in organ (same group)"
title_vs <- sprintf("%s vs. same-group non-organ", organ)
}
rate_df$rate_in_organ <- rate_df$n_pos_in_organ / rate_df$n_in_organ
rate_df$rate_ref <- rate_df$n_pos_ref / rate_df$n_ref
rate_df <- rate_df[rate_df$n_in_organ >= min_in_organ, , drop = FALSE]
if (nrow(rate_df) == 0) stop(sprintf("No %s type has >= %d cells in '%s'.", group_label, min_in_organ, organ))
long_df <- data.frame(
group = rep(rate_df$group, 2),
type = factor(rep(c("In organ", ref_label), each = nrow(rate_df)), levels = c("In organ", ref_label)),
rate = c(rate_df$rate_in_organ, rate_df$rate_ref)
)
level_order <- rate_df$group[order(rate_df$rate_in_organ, decreasing = TRUE)]
long_df$group <- factor(long_df$group, levels = level_order)
fill_values <- group_colors[as.character(level_order)]
missing_colors <- is.na(fill_values)
if (any(missing_colors)) {
fill_values[missing_colors] <- fallback_group_color
warning(sprintf(
"%s annotations missing from group_colors and colored %s: %s",
group_label, fallback_group_color, paste(level_order[missing_colors], collapse = ", ")
))
}
alpha_vals <- c(1, 0.35)
names(alpha_vals) <- c("In organ", ref_label)
ggplot(long_df, aes(x = group, y = rate, fill = group, alpha = type)) +
geom_col(position = position_dodge(width = 0.8), width = 0.75, color = "grey35", linewidth = 0.15) +
scale_fill_manual(values = fill_values, guide = "none") +
scale_alpha_manual(values = alpha_vals, guide = guide_legend(override.aes = list(fill = "grey40"))) +
labs(
x = sprintf("%s annotation", group_label),
y = sprintf("Proportion of cells with %s > %.3g", gp, threshold),
alpha = NULL,
title = sprintf("%s+ rate by %s: %s", gp, group_label, title_vs),
subtitle = sprintf("threshold = %.3g; %s types with < %d cells in %s dropped", threshold, group_label, min_in_organ, organ)
) +
theme_minimal(base_size = base_size) +
theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "top")
}
p_6c <- plot_gp_threshold_group_activation_rate(
gp = "GP37",
organ = "mammary gland",
threshold = organ_AUC_list$threshold["mammary gland", "GP37"],
min_in_organ = 100,
loading_mat = L_pm_filtered[rownames(seurat_meta_filtered_no_thymocytes_healthy), ],
organ_info = seurat_meta_filtered_no_thymocytes_healthy$organ_simplified,
group_info = seurat_meta_filtered_no_thymocytes_healthy$annotation_level1,
group_label = "Level-1",
group_colors = ZemmourLib::immgent_colors$level1,
reference = "not_in_organ"
)
ggsave(filename = paste0(figure_path, "6c.pdf"), plot = p_6c, width = 8, height = 5, dpi = 300)
Fig. 6c. Proportion of GP37-active cells in each lineage in the mammary gland compared with all other organs (faded color bar).
(d) Max AUC: organ vs. cluster
# ============================================================
# 6d prep: Max AUC Organ vs Level-2
# ============================================================
level_2_AUC <- level_2_AUC_list$auc
level_2_small_count <- table(seurat_meta_filtered_no_thymocytes_healthy$annotation_level2)
level_2_small_count <- names(level_2_small_count[level_2_small_count < 100])
level_2_AUC <- level_2_AUC[!rownames(level_2_AUC) %in% level_2_small_count, ]
level_2_cat_mean <- t(sapply(rownames(level_2_AUC), function(cat) {
idx <- seurat_meta_filtered_no_thymocytes_healthy$annotation_level2 == cat
colMeans(L_healthy[idx, , drop = FALSE], na.rm = TRUE)
}))
level_2_AUC_positive <- sweep(level_2_cat_mean, 2, overall_mean, "-") > 0
# 6d/6e reuse `organ_AUC_max_name`, but recomputed against the Level-2
# category-count filter.
organ_AUC_masked_l2 <- organ_AUC
organ_AUC_positive_l2 <- sweep(
t(sapply(rownames(organ_AUC), function(cat) {
idx <- seurat_meta_filtered_no_thymocytes_healthy$organ_simplified == cat
colMeans(L_healthy[idx, , drop = FALSE], na.rm = TRUE)
})),
2, overall_mean, "-"
) > 0
organ_AUC_masked_l2[!organ_AUC_positive_l2] <- NA
organ_AUC_max <- apply(organ_AUC_masked_l2, 2, max, na.rm = TRUE)
organ_AUC_max_name <- apply(organ_AUC_masked_l2, 2, function(x) rownames(organ_AUC_masked_l2)[which.max(x)])
# 6d's own max-AUC table, over the Level-2 categories.
level_2_AUC_masked <- level_2_AUC
level_2_AUC_masked[!level_2_AUC_positive] <- NA
level_2_AUC_max <- apply(level_2_AUC_masked, 2, max, na.rm = TRUE)
level_2_AUC_max_name <- apply(level_2_AUC_masked, 2, function(x) {
rownames(level_2_AUC_masked)[which.max(x)]
})
o_l2 <- order(level_2_AUC_max, decreasing = TRUE)
table_level_2_AUC <- data.frame(
Factor = colnames(level_2_AUC)[o_l2],
Max_AUC = level_2_AUC_max[o_l2],
Annotation = level_2_AUC_max_name[o_l2]
)
df_l2 <- data.frame(
Factor = table_level_2_AUC$Factor,
annotation_Level2 = table_level_2_AUC$Annotation,
annotation_Organ = organ_AUC_max_name[match(table_level_2_AUC$Factor, names(organ_AUC_max))],
Max_AUC_Organ = organ_AUC_max[match(table_level_2_AUC$Factor, names(organ_AUC_max))],
Max_AUC_Level2 = table_level_2_AUC$Max_AUC
) %>%
mutate(residual = Max_AUC_Level2 - Max_AUC_Organ, abs_res = abs(residual))
# ============================================================
# 6d: Max AUC Organ vs Level-2, 7 organ-specific GPs (red) vs.
# contrasting cluster-specific GPs (blue) highlighted
# ============================================================
seven_gp_df <- df_l2 |>
dplyr::filter(Factor %in% gps_of_interest) |>
dplyr::mutate(label_text = sapply(Factor, top_cats_label, auc_matrix = level_2_AUC, positive_mask = level_2_AUC_positive, threshold = 0.9, n = 3))
top_left_gps <- c("GP14", "GP36", "GP16", "GP151", "GP21", "GP122", "GP2", "GP171", "GP5", "GP13")
top_left_df <- df_l2 |>
dplyr::filter(Factor %in% top_left_gps) |>
dplyr::mutate(label_text = sapply(Factor, top_cats_label, auc_matrix = level_2_AUC, positive_mask = level_2_AUC_positive, threshold = 0.9, n = 3))
p_6d <- ggplot(df_l2, aes(Max_AUC_Organ, Max_AUC_Level2)) +
geom_point(alpha = 0.2, size = 1.5, color = "grey60") +
geom_point(data = top_left_df, color = "#1f78b4", size = 2.2, alpha = 0.9) +
geom_text_repel(
seed = 42,
data = top_left_df, aes(label = label_text), color = "#1f78b4", lineheight = 0.85, size = 2.5,
direction = "y", nudge_x = -0.1, segment.color = "#1f78b4",
arrow = arrow(length = unit(0.008, "npc"), type = "closed", angle = 20),
force = 4, force_pull = 0.05, box.padding = 0.5, point.padding = 0.15,
max.time = 10, max.iter = 2e4, max.overlaps = 30, min.segment.length = 0.01, segment.alpha = 0.7
) +
geom_point(data = seven_gp_df, color = "#e31a1c", size = 2.2, alpha = 0.9) +
geom_text_repel(
seed = 42,
data = seven_gp_df, aes(label = label_text), color = "#e31a1c", size = 2.5, lineheight = 0.85,
direction = "y", nudge_x = 0.18, xlim = c(1.0, NA), segment.color = "#e31a1c",
arrow = arrow(length = unit(0.008, "npc"), type = "closed", angle = 20),
force = 6, force_pull = 0.02, box.padding = 0.6, point.padding = 0.15,
max.time = 10, max.iter = 2e4, max.overlaps = 30, min.segment.length = 0.01, segment.alpha = 0.7
) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black") +
coord_cartesian(xlim = c(0.46, 1.04), ylim = c(0.5, 1.02), expand = FALSE, clip = "off") +
labs(x = "Max AUC (Organ Simplified)", y = "Max AUC (Level-2)", title = "Max AUC: Organ vs Level-2 - organ-specific GPs") +
theme_minimal(base_size = 13) +
theme(plot.margin = margin(10, 80, 10, 80))
ggsave(filename = paste0(figure_path, "6d.pdf"), plot = p_6d, width = 8, height = 8, dpi = 300)
Fig. 6d. Scatterplot showing each GP’s prediction (AUC) of organ of origin (x-axis) versus level 2 cluster (y-axis). The seven organ-specific GPs are highlighted in red, and a contrasting set of cluster-specific GPs (high cluster AUC, low organ AUC) in blue.
(e) Organ, GP, and cluster alluvial diagram
# ============================================================
# 6e: alluvial, organ -> GP -> Level-2, for GP+ cells of the
# 7 organ-specific GPs
# ============================================================
best_organ_per_gp <- organ_AUC_max_name[gps_of_interest]
gp_thresholds <- mapply(function(gp, organ) organ_AUC_list$threshold[organ, gp], gps_of_interest, best_organ_per_gp)
names(gp_thresholds) <- gps_of_interest
n_cap_gp <- 300
set.seed(42)
alluvial_rows <- lapply(gps_of_interest, function(gp) {
positive_idx <- L_healthy[, gp] > gp_thresholds[gp]
meta_pos <- seurat_meta_filtered_no_thymocytes_healthy[positive_idx, ]
d <- data.frame(gp_program = gp, organ = meta_pos$organ_simplified, level2 = meta_pos$annotation_level2, stringsAsFactors = FALSE)
if (nrow(d) > n_cap_gp) d <- dplyr::slice_sample(d, n = n_cap_gp)
d
})
count_df <- do.call(rbind, alluvial_rows) |>
dplyr::count(organ, gp_program, level2, name = "n") |>
dplyr::filter(!is.na(organ), !is.na(level2), n >= 5)
organ_order <- count_df |> dplyr::summarise(total = sum(n), .by = organ) |> dplyr::arrange(dplyr::desc(total)) |> dplyr::pull(organ)
level2_order <- count_df |> dplyr::summarise(total = sum(n), .by = level2) |> dplyr::arrange(dplyr::desc(total)) |> dplyr::pull(level2)
count_df <- count_df |>
dplyr::mutate(
organ = factor(organ, levels = rev(organ_order)),
gp_program = factor(gp_program, levels = rev(gps_of_interest)),
level2 = factor(level2, levels = rev(level2_order))
)
gp_colors <- ZemmourLib::immgent_colors$organ_simplified[unname(best_organ_per_gp)]
gp_colors[is.na(gp_colors)] <- "grey60"
names(gp_colors) <- gps_of_interest
p_6e <- ggplot(count_df, aes(axis1 = organ, axis2 = gp_program, axis3 = level2, y = n)) +
ggalluvial::geom_alluvium(aes(fill = gp_program), width = 1 / 4, alpha = 0.6, knot.pos = 0.4) +
ggalluvial::geom_stratum(width = 1 / 4, fill = "grey92", color = "grey50", linewidth = 0.3) +
ggplot2::geom_text(stat = ggalluvial::StatStratum, aes(label = after_stat(stratum)), size = 3, angle = 90) +
scale_fill_manual(values = gp_colors, guide = "none") +
scale_x_discrete(limits = c("Organ", "GP", "Level-2"), expand = c(0.12, 0.12)) +
labs(y = "Number of GP+ cells", title = "GP+ cells: organ origin and cell type") +
theme_minimal(base_size = 12) +
theme(panel.grid = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank()) +
coord_flip()
ggsave(filename = paste0(figure_path, "6e.pdf"), plot = p_6e, width = 20, height = 10, dpi = 300)
Fig. 6e. Alluvial plot showing how tissue-specific GP activity is distributed across clusters. For each organ, GP-active cells are traced to their cluster annotation. Flows are colored by GP, and width is proportional to the number of cells.
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