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 |
|---|---|---|---|---|
| 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, shared across all panels below.
# Figure 6. GPs and tissue.
#
# Panels produced:
# 6a Max AUC (organ) vs max AUC (level-1 lineage) scatter, per GP.
# 6b GP37+ rate by lineage, mammary gland vs. the same lineage elsewhere.
# 6c Marker genes of the 7 organ-specific GPs: per-GP gene-score heatmap.
# 6d As 6a, but 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, F_pm_filtered.rds [code/pipeline/01b_filter_cells.R]
# igt1_96_..._ADTonly.Rds [primary input Seurat object]
# level_1_AUC_list_figure_no_thymocytes_healthy.rds,
# 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)
data_path <- "data/"
figure_path <- "figures/final-selected/Figure 6/"
# ============================================================
# Load data (healthy, non-thymocyte reference)
# ============================================================
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"
))
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_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))
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")
# 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"))
}(a) Max AUC: organ vs. lineage
# ============================================================
# 6a: Max AUC Organ vs Level-1
# ============================================================
level_1_small_count <- table(seurat_meta_filtered_no_thymocytes_healthy$annotation_level1)
level_1_small_count <- names(level_1_small_count[level_1_small_count < 1000])
level_1_AUC <- level_1_AUC_list$auc
level_1_AUC <- level_1_AUC[!rownames(level_1_AUC) %in% level_1_small_count, ]
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, ]
# Positivity masks: mean loading in category > overall mean -> high loading predicts membership
healthy_cells <- rownames(seurat_meta_filtered_no_thymocytes_healthy)
L_healthy <- L_pm_filtered[healthy_cells, ]
overall_mean <- colMeans(L_healthy, na.rm = TRUE)
level_1_cat_mean <- t(sapply(rownames(level_1_AUC), function(cat) {
idx <- seurat_meta_filtered_no_thymocytes_healthy$annotation_level1 == cat
colMeans(L_healthy[idx, , drop = FALSE], na.rm = TRUE)
}))
level_1_AUC_positive <- sweep(level_1_cat_mean, 2, overall_mean, "-") > 0
organ_cat_mean <- t(sapply(rownames(organ_AUC), function(cat) {
idx <- seurat_meta_filtered_no_thymocytes_healthy$organ_simplified == cat
colMeans(L_healthy[idx, , drop = FALSE], na.rm = TRUE)
}))
organ_AUC_positive <- sweep(organ_cat_mean, 2, overall_mean, "-") > 0
level_1_AUC_masked <- level_1_AUC
level_1_AUC_masked[!level_1_AUC_positive] <- NA
level_1_AUC_max <- apply(level_1_AUC_masked, 2, max, na.rm = TRUE)
level_1_AUC_max_name <- apply(level_1_AUC_masked, 2, function(x) rownames(level_1_AUC_masked)[which.max(x)])
o <- order(level_1_AUC_max, decreasing = TRUE)
table_level_1_AUC <- data.frame(
Factor = colnames(level_1_AUC)[o], Max_AUC = level_1_AUC_max[o], Annotation = level_1_AUC_max_name[o]
)
organ_AUC_masked <- organ_AUC
organ_AUC_masked[!organ_AUC_positive] <- NA
organ_AUC_max <- apply(organ_AUC_masked, 2, max, na.rm = TRUE)
organ_AUC_max_name <- apply(organ_AUC_masked, 2, function(x) rownames(organ_AUC_masked)[which.max(x)])
max_AUC_df <- data.frame(
Factor = table_level_1_AUC$Factor,
annotation_Level1 = table_level_1_AUC$Annotation,
annotation_Organ = organ_AUC_max_name[match(table_level_1_AUC$Factor, names(organ_AUC_max))],
Max_AUC_Organ = organ_AUC_max[match(table_level_1_AUC$Factor, names(organ_AUC_max))],
Max_AUC_Level1 = table_level_1_AUC$Max_AUC
)
df <- max_AUC_df %>% mutate(residual = Max_AUC_Level1 - Max_AUC_Organ, abs_res = abs(residual))
# Factors to highlight: AUC > 0.9 in at least one axis (organ or level-1)
highlighted_factors <- df %>%
filter(is.finite(residual), Max_AUC_Organ > 0.9 | Max_AUC_Level1 > 0.9) %>%
pull(Factor)
label_above <- df %>%
filter(Factor %in% highlighted_factors, residual > 0) %>%
mutate(nudge_x = -0.035, label_text = sapply(Factor, top_cats_label, auc_matrix = level_1_AUC, positive_mask = level_1_AUC_positive))
label_below <- df %>%
filter(Factor %in% highlighted_factors, residual <= 0) %>%
mutate(nudge_x = 0.035, label_text = sapply(Factor, top_cats_label, auc_matrix = organ_AUC, positive_mask = organ_AUC_positive))
p_6a <- ggplot(df, aes(Max_AUC_Organ, Max_AUC_Level1)) +
geom_point(alpha = 0.3, size = 1.8) +
geom_point(data = label_above, color = "#1f78b4", alpha = 0.8, size = 1.8) +
geom_point(data = label_below, color = "#e31a1c", alpha = 0.8, size = 1.8) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black") +
coord_cartesian(xlim = c(0.46, 1.04), ylim = c(0.5, 1.02), expand = FALSE) +
labs(x = "Max AUC (Organ Simplified)", y = "Max AUC (Level-1)", title = "Max AUC: Organ vs Level-1") +
theme_minimal(base_size = 13) +
geom_text_repel(
seed = 42,
data = label_above, aes(label = label_text), color = "#1f78b4", size = 2.5, lineheight = 0.85,
direction = "y", nudge_x = label_above$nudge_x, 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 = label_below$nudge_x, segment.color = "#e31a1c",
arrow = arrow(length = unit(0.008, "npc"), type = "closed", angle = 20),
force = 3, force_pull = 0.1, box.padding = 0.4, point.padding = 0.15,
max.time = 10, max.iter = 2e4, max.overlaps = 20, min.segment.length = 0.01, segment.alpha = 0.7
)
ggsave(filename = paste0(figure_path, "6a.pdf"), plot = p_6a, width = 8, height = 8, dpi = 300)
| Version | Author | Date |
|---|---|---|
| 5874416 | Ziang Zhang | 2026-09-10 |
Fig. 6a. Scatterplot showing each GP’s prediction (AUC) of organ of origin (x-axis) versus lineage (y-axis). GPs reaching AUC > 0.9 on either axis are colored and labeled (organ-specific in red; lineage-specific in blue).
(b) GP37+ rate in mammary gland vs. elsewhere
# ============================================================
# 6b: GP37+ rate by lineage, mammary gland vs. elsewhere
# ============================================================
plot_gp_threshold_group_activation_rate <- function(
gp, organ, threshold, loading_mat, organ_info, group_info,
group_label = "Level-2", base_size = 13, min_in_organ = 10,
group_colors = ZemmourLib::immgent_colors$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_6b <- 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, "6b.pdf"), plot = p_6b, width = 8, height = 5, dpi = 300)
Fig. 6b. Proportion of GP37-active cells in each lineage in the mammary gland compared with all other organs (faded color bar).
(c) Marker genes of the 7 organ-specific GPs
# ============================================================
# 6c: organ marker genes - per-GP gene-score heatmap
# ============================================================
F_pm_filtered <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
colnames(F_pm_filtered) <- gsub("^F", "GP", colnames(F_pm_filtered))
D_scale <- diag(1 / apply(F_pm_filtered, 2, function(x) max(abs(x), na.rm = TRUE)))
F_pm_scaled <- F_pm_filtered %*% D_scale
colnames(F_pm_scaled) <- colnames(F_pm_filtered)
n_top_genes <- 20
min_loading <- 0.25
F_sub <- F_pm_scaled[, gps_of_interest, drop = FALSE]
selected_genes <- lapply(gps_of_interest, function(gp) {
vals <- F_sub[, gp]
names(sort(vals[vals > min_loading], decreasing = TRUE))[seq_len(min(n_top_genes, sum(vals > min_loading)))]
})
selected_genes <- unique(unlist(selected_genes))
# Diagonal gene ordering by dominant GP (highest loading)
GP_orders <- c("GP37", "GP26", "GP6", "GP177", "GP3", "GP29", "GP11")
dominant_gp <- apply(F_sub[selected_genes, , drop = FALSE], 1, function(x) GP_orders[which.max(x[GP_orders])])
dominant_loading <- mapply(function(g, gp) F_sub[g, gp], selected_genes, dominant_gp)
gene_order_df <- data.frame(Gene = selected_genes, dominant_gp = factor(dominant_gp, levels = GP_orders), loading = dominant_loading, stringsAsFactors = FALSE)
gene_order_df <- gene_order_df[order(gene_order_df$dominant_gp, -gene_order_df$loading), ]
heatmap_gene_order <- gene_order_df$Gene
plot_df_hm <- as.data.frame(F_sub[heatmap_gene_order, , drop = FALSE])
plot_df_hm$Gene <- rownames(plot_df_hm)
plot_df_hm <- tidyr::pivot_longer(plot_df_hm, cols = -Gene, names_to = "GP", values_to = "Loading")
plot_df_hm$GP <- factor(plot_df_hm$GP, levels = GP_orders)
plot_df_hm$Gene <- factor(plot_df_hm$Gene, levels = rev(heatmap_gene_order))
limit_hm <- max(abs(plot_df_hm$Loading), na.rm = TRUE)
p_gene_heatmap <- ggplot(plot_df_hm, aes(x = GP, y = Gene, fill = Loading)) +
geom_tile() +
scale_fill_gradient2(low = "steelblue", mid = "white", high = "firebrick", midpoint = 0, limits = c(-limit_hm, limit_hm), name = "Loading") +
labs(title = "Top positive genes per organ GP", x = NULL, y = NULL) +
theme_minimal(base_size = 9) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9), axis.text.y = element_text(size = 8), panel.grid = element_blank(), plot.title = element_text(face = "bold", size = 11))
pdf(paste0(figure_path, "6c.pdf"), width = 5, height = 16, useDingbats = FALSE)
print(p_gene_heatmap)
dev.off()
Fig. 6c. Genes associated with the seven organ-specific GPs shown in (a). Heatmap of the per-GP scaled gene scores for the top upregulated genes of each GP (top 20 per GP, gene score > 0.25; blue, negative; red, positive).
(d) Max AUC: organ vs. cluster
# ============================================================
# 6d prep: Max AUC Organ vs Level-2
# ============================================================
level_2_AUC <- level_2_AUC_list$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. As in (a), but predictive performance is measured against level 2 clusters (y-axis). The seven organ-specific GPs from (a) 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