Extended Data Figure 3: Characterizing activation-associated GPs
Ziang Zhang
Last updated: 2026-08-19
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immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 |
| 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 | 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 | fa5a501 | Ziang Zhang | 2026-07-18 | Fig S3c: reproduce collaborator panel (activated CD4/CD8, threshold 0.1) |
| html | fa5a501 | Ziang Zhang | 2026-07-18 | Fig S3c: reproduce collaborator panel (activated CD4/CD8, threshold 0.1) |
| 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 | 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 |
Panels (b)-(g) are produced by script/FigureS3.R,
which shares its curated GP set with Figure 4
via code/R/activation_shared_setup.R. The code below is
shown for reference (not re-executed on this page); the images are its
pre-rendered output.
Panel (a) is not produced in this repository – see below.
Setup
# Figure S3. Characterizing activation GPs.
#
# Panels produced:
#
# s3a is the definition of resting vs activated CD4/CD8 cells (the
# lineage-specific MDEs plus the adjacent CD62L-vs-CD44 protein plot, one
# panel). It is NOT produced here -- no code in this repository draws it; see
# analysis/FigureS3.Rmd.
#
# s3b Fraction of activated CD4/CD8 cells per organ with GP26 > 0.1, sorted.
# s3c GSEA dot plot relating the activation GPs to curated gene sets.
# s3d Fraction of activated CD4 cells with GP79 loading > 0.1, by condition.
# s3e Per-cell log2FC heatmap of activation GPs vs resting baseline.
# s3f Fraction of activated CD8/CD4 cells with GP57 loading > 0.1: cancer
# vs all other conditions.
# s3g Bipartite TF-GP network for Gata3, Rorc, Tbx21.
#
# The curated GP set and activated/resting cell groupings are shared with
# Figure 4 via code/R/activation_shared_setup.R.
#
# Panel s3b is computed on activated CD4/CD8 cells only (annotation_level1 in
# {CD4, CD8} and annotation_level2_group == "activated"): the per-organ fraction
# with GP26 loading > 0.1.
#
# 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]
# GSEA_signatures_select_toplot.csv [external: curated gene-set collection]
library(ggplot2)
library(ggrepel)
library(dplyr)
library(stringr)
library(pheatmap)
library(scales)
data_path <- "data/"
figure_path <- "figures/final-selected/Figure S3/"
source("code/R/plot_utils.R") # scale_cols()
# ============================================================
# Load data
# ============================================================
L_pm_filtered <- readRDS(paste0(data_path, "L_pm_filtered.rds"))
colnames(L_pm_filtered) <- paste0("GP", seq_len(ncol(L_pm_filtered)))
F_pm_filtered <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
colnames(F_pm_filtered) <- paste0("GP", seq_len(ncol(F_pm_filtered)))
seurat_meta <- readRDS(paste0(data_path, "igt1_96_withtotalvi20260206_clean_ADTonly.Rds"))@meta.data
seurat_meta_filtered <- seurat_meta[rownames(L_pm_filtered), ]
df_sig <- read.csv(paste0(data_path, "GSEA_signatures_select_toplot.csv"), header = TRUE, sep = ",")
source("code/R/activation_shared_setup.R")(a) Definition of resting and activated CD4/CD8 cells
This panel shows the CITE-seq gating used to define resting and activated cells; it is not generated from code, so only its caption is given here.
Extended Data Fig. 3a. Definition of resting and activated CD4+ and CD8+ T cells. Activated CD44+CD62L- (top) and resting CD62L+CD44- (bottom) cells are highlighted in each lineage-specific MDE, and the gated cells are shown in the adjacent CD62L versus CD44 protein-expression plot (CITE-seq; log-normalized ADT counts).
The annotation_level2_group “activated”/“resting” labels
that every other panel on this page (and all of Figure 4) filters on are the labels this panel
defines, so the gate itself is already in use here – only the figure
illustrating it is absent.
(b) GP26+ rate by organ
# ============================================================
# s3b: Fraction of activated CD4/CD8 cells per organ with GP26 loading > 0.1
# ============================================================
act_cd4_cd8 <- seurat_meta_filtered$annotation_level1 %in% c("CD4", "CD8") &
seurat_meta_filtered$annotation_level2_group == "activated"
gp26_organ_df <- data.frame(
organ = seurat_meta_filtered$organ_simplified[act_cd4_cd8],
gp26_high = L_pm_filtered[act_cd4_cd8, "GP26"] > 0.1
) %>%
filter(!is.na(organ), organ != "") %>%
group_by(organ) %>%
summarise(n_cells = n(), proportion_gp26_high = mean(gp26_high), .groups = "drop") %>%
arrange(desc(proportion_gp26_high)) %>%
mutate(organ = factor(organ, levels = organ),
pct = 100 * proportion_gp26_high,
pct_lab = sub("\\.0%$", "%", sprintf("%.1f%%", pct))) # e.g. 99%, 98.7%
# abbreviate a few long organ names to match the published panel
organ_abbrev <- c("submandibular gland" = "submand. gland",
"mammary gland" = "mam. gland",
"small intestine epi" = "sm intestine epi",
"small intestine LP" = "sm intestine LP",
"peritoneal cavity" = "perit. cav.")
p_s3b <- ggplot(gp26_organ_df, aes(x = organ, y = pct)) +
geom_col(fill = "#4C72B0", width = 0.7) +
geom_text(aes(label = pct_lab), vjust = -0.4, size = 3, color = "grey20") +
scale_x_discrete(labels = function(x) ifelse(x %in% names(organ_abbrev), organ_abbrev[x], x)) +
scale_y_continuous(breaks = c(0, 50, 100), labels = c("0", "50%", "100"),
expand = expansion(mult = c(0, 0.10))) +
labs(x = NULL, y = "Proportion activated (loading > 0.1) (%)",
title = "Activation of GP26 (threshold = 0.1) across Organ") +
theme_classic(base_size = 12) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5))
ggsave(filename = paste0(figure_path, "s3b.pdf"), plot = p_s3b, width = 9, height = 4.8)
| Version | Author | Date |
|---|---|---|
| d538aa2 | Ziang Zhang | 2026-07-28 |
Extended Data Fig. 3b. Bar plot showing the
percentage of activated CD4+ and CD8+ T cells in each
organ (organ_simplified) with GP26 activity above
0.1, sorted from highest to lowest. Activated cells are
those with annotation_level2_group == "activated" among the
CD4 and CD8 lineages.
(c) GSEA dot plot
# ============================================================
# s3c: GSEA dot plot for the activation GPs
# ============================================================
df_sig <- df_sig %>% mutate(factor = str_replace(factor, "^F", "GP"))
present_GPs <- intersect(ordered_GPs, unique(df_sig$factor))
y_levels <- rev(present_GPs) # first GP (GP56) appears at the top of the y-axis
df_plot <- df_sig %>%
filter(factor %in% present_GPs) %>%
mutate(pathway = factor(pathway, levels = unique(pathway)), factor = factor(factor, levels = y_levels), log10padj = -log10(padj))
y_factors <- levels(df_plot$factor)
y_colors <- highlight_colors[y_factors]
p_s3c <- ggplot(df_plot, aes(x = pathway, y = factor)) +
geom_point(aes(size = NES, color = log10padj)) +
scale_color_viridis_c(name = "-log10(p-adj)") +
scale_size(range = c(3, 10)) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1), axis.text.y = element_text(color = y_colors, face = "bold")) +
labs(size = "NES", x = "Pathway", y = "Gene Program")
ggsave(filename = paste0(figure_path, "s3c.pdf"), plot = p_s3c, width = 8, height = 10)
Extended Data Fig. 3c. Gene set enrichment analysis (GSEA) dot plot relating activation-associated GPs to curated immunologic signature gene sets. Each dot is a significant GP-gene-set association; color denotes -log10 adjusted P value and dot size denotes the normalized enrichment score (NES). GP labels are colored by activation class.
(d) GP79+ rate across conditions
# ============================================================
# s3d: GP79 high-loading proportion across conditions, activated CD4 cells
# ============================================================
min_cells_gp79_condition <- 50
gp79_cd4_condition_df <- data.frame(
condition_broad = as.character(seurat_meta_filtered$condition_broad),
condition_detailed_simplified = as.character(seurat_meta_filtered$condition_detailed_simplified),
lineage = seurat_meta_filtered$annotation_level1,
activation_group = seurat_meta_filtered$annotation_level2_group,
gp79_loading = L_pm_filtered[, "GP79"],
stringsAsFactors = FALSE
) %>%
filter(lineage == "CD4", activation_group == "activated", !is.na(condition_detailed_simplified), condition_detailed_simplified != "") %>%
mutate(gp79_high = gp79_loading > 0.1)
gp79_cd4_condition_summary <- gp79_cd4_condition_df %>%
group_by(condition_detailed_simplified) %>%
summarise(
condition_broad = names(sort(table(condition_broad), decreasing = TRUE))[1],
n_cells = n(), n_gp79_high = sum(gp79_high), proportion_gp79_high = mean(gp79_high), .groups = "drop"
) %>%
mutate(included_in_plot = n_cells >= min_cells_gp79_condition) %>%
arrange(desc(proportion_gp79_high), condition_detailed_simplified)
gp79_cd4_condition_plot_df <- gp79_cd4_condition_summary %>%
filter(included_in_plot) %>%
arrange(proportion_gp79_high, condition_detailed_simplified) %>%
mutate(condition_label = factor(condition_detailed_simplified, levels = condition_detailed_simplified))
gp79_broad_levels <- sort(unique(gp79_cd4_condition_plot_df$condition_broad))
gp79_broad_colors <- setNames(scales::hue_pal()(length(gp79_broad_levels)), gp79_broad_levels)
gp79_xmax <- max(gp79_cd4_condition_plot_df$proportion_gp79_high, na.rm = TRUE)
gp79_xmax <- ifelse(gp79_xmax > 0, gp79_xmax, 0.05)
gp79_label_pad <- gp79_xmax * 0.015
gp79_plot_height <- max(7, 0.18 * nrow(gp79_cd4_condition_plot_df) + 2)
p_s3d <- ggplot(gp79_cd4_condition_plot_df, aes(x = proportion_gp79_high, y = condition_label, fill = condition_broad)) +
geom_col(width = 0.75, color = "grey25", linewidth = 0.2) +
geom_text(aes(x = proportion_gp79_high + gp79_label_pad, label = scales::percent(proportion_gp79_high, accuracy = 1)), hjust = 0, size = 2.7) +
scale_x_continuous(labels = scales::percent_format(accuracy = 10), expand = expansion(mult = c(0, 0.02))) +
scale_fill_manual(values = gp79_broad_colors) +
coord_cartesian(xlim = c(0, gp79_xmax * 1.15), clip = "off") +
labs(
x = "Proportion of activated CD4 cells with GP79 loading > 0.1", y = NULL, fill = "Condition broad",
title = "GP79-high fraction across activated CD4 conditions",
subtitle = paste0("condition_detailed_simplified categories with >= ", min_cells_gp79_condition, " activated CD4 cells")
) +
theme_classic(base_size = 11) +
theme(
legend.position = "right", legend.title = element_text(face = "bold"),
plot.title = element_text(face = "bold", hjust = 0.5), plot.subtitle = element_text(hjust = 0.5),
axis.text.y = element_text(size = 8), plot.margin = margin(10, 45, 10, 10)
)
ggsave(filename = paste0(figure_path, "s3d.pdf"), plot = p_s3d, width = 9, height = gp79_plot_height, limitsize = FALSE)
Extended Data Fig. 3d. Bar plot showing the
percentage of GP79-active cells (GP79 activity above 0.1) among all
activated CD4+ T cells across immunological challenges – condition
categories (condition_detailed_simplified) that contain
>= 50 activated CD4 cells, sorted from highest to lowest and colored
by broad condition category.
(e) Per-cell log2FC heatmap
# ============================================================
# s3e: Per-cell log2FC heatmap of activation GPs (activated cells only),
# relative to each cell's own-lineage resting baseline
# ============================================================
L <- L_pm_filtered[c(CD4_cells, CD8_cells), GPs_of_interest, drop = FALSE]
cell_ids <- rownames(L)
cell_type <- seurat_meta_filtered$annotation_level2[match(cell_ids, seurat_meta_filtered$cellID)]
set.seed(123)
total_budget <- 8000
small_keep_all <- 150
min_per_cluster <- 150
max_per_cluster <- 600
cells_by_ct <- split(cell_ids, cell_type)
sizes <- sapply(cells_by_ct, length)
alloc <- ifelse(sizes <= small_keep_all, sizes, pmin(min_per_cluster, sizes))
remaining <- total_budget - sum(alloc)
if (remaining > 0) {
room <- pmax(pmin(sizes, max_per_cluster) - alloc, 0)
if (sum(room) > 0) {
extra <- floor(remaining * room / sum(room))
alloc <- alloc + extra
leftover <- total_budget - sum(alloc)
if (leftover > 0) {
idx <- order(room, decreasing = TRUE)
for (j in idx) {
if (leftover <= 0) break
addable <- pmin(room[j] - extra[j], leftover)
if (addable > 0) {
alloc[j] <- alloc[j] + addable
leftover <- leftover - addable
}
}
}
}
}
sampled_cells <- unlist(mapply(function(v, m) if (length(v) <= m) v else sample(v, m), cells_by_ct, pmin(alloc, sizes), SIMPLIFY = FALSE), use.names = FALSE)
cell_group_s <- seurat_meta_filtered$annotation_level2_group[match(sampled_cells, seurat_meta_filtered$cellID)]
cell_level2_s <- seurat_meta_filtered$annotation_level2[match(sampled_cells, seurat_meta_filtered$cellID)]
cell_group_s <- trimws(tolower(as.character(cell_group_s)))
cell_level2_s <- trimws(as.character(cell_level2_s))
cell_level1_s <- sapply(strsplit(cell_level2_s, "[_.]"), `[`, 1)
is_w_cell <- grepl("\\.w", cell_level2_s)
valid_idx <- which(!is.na(cell_level2_s) & !tolower(cell_level2_s) %in% c("", "na", "nan") & cell_group_s %in% c("resting", "activated") & !is_w_cell)
final_cells <- sampled_cells[valid_idx]
final_group <- cell_group_s[valid_idx]
final_level1 <- cell_level1_s[valid_idx]
final_level2 <- cell_level2_s[valid_idx]
col_order <- order(final_group, final_level1, final_level2)
final_cells <- final_cells[col_order]
final_group <- final_group[col_order]
final_level1 <- final_level1[col_order]
final_level2 <- final_level2[col_order]
pc <- 1e-10
cap <- 2
group_counts <- sapply(
list(
c("GP56", "GP162", "GP36", "GP152", "GP161", "GP177", "GP79", "GP12", "GP13", "GP159"),
c("GP10", "GP58", "GP181", "GP176"),
c("GP25", "GP26", "GP35", "GP32", "GP80", "GP57"),
c("GP9", "GP171", "GP49", "GP41", "GP11")
),
function(gp_group) sum(ordered_GPs %in% gp_group)
)
group_counts <- group_counts[group_counts > 0]
gaps_row <- cumsum(group_counts)[-length(group_counts)]
act_idx <- which(final_group == "activated")
act_cells <- final_cells[act_idx]
act_level1 <- final_level1[act_idx]
act_level2 <- final_level2[act_idx]
act_order <- order(act_level1, act_level2)
act_cells <- act_cells[act_order]
act_level1 <- act_level1[act_order]
act_level2 <- act_level2[act_order]
L_sub_act <- L[act_cells, , drop = FALSE]
M_raw_act <- t(L_sub_act)
# Per-lineage resting baseline: each activated cell's log2FC is computed
# against the mean loading in resting cells of its own lineage.
mu_resting_CD4 <- colMeans(L_pm_filtered[CD4_resting_cells, GPs_of_interest, drop = FALSE])
mu_resting_CD8 <- colMeans(L_pm_filtered[CD8_resting_cells, GPs_of_interest, drop = FALSE])
baseline_mat <- matrix(NA_real_, nrow = nrow(M_raw_act), ncol = ncol(M_raw_act))
rownames(baseline_mat) <- rownames(M_raw_act)
baseline_mat[, act_level1 == "CD4"] <- mu_resting_CD4[rownames(M_raw_act)]
baseline_mat[, act_level1 == "CD8"] <- mu_resting_CD8[rownames(M_raw_act)]
M_fc_act <- log2((M_raw_act + pc) / (baseline_mat + pc))
M_fc_act_cap <- pmax(pmin(M_fc_act, cap), -cap)
M_fc_act_cap <- M_fc_act_cap[ordered_GPs, , drop = FALSE]
ann_col_act <- data.frame(Level2 = factor(act_level2), Level1 = factor(act_level1))
rownames(ann_col_act) <- colnames(M_fc_act_cap)
present_level1_act <- levels(ann_col_act$Level1)
present_level2_act <- levels(ann_col_act$Level2)
level1_cols_act <- ZemmourLib::immgent_colors$level1
level1_cols_act <- level1_cols_act[names(level1_cols_act) %in% present_level1_act]
level2_cols_act <- ZemmourLib::immgent_colors$level2
level2_cols_act <- level2_cols_act[names(level2_cols_act) %in% present_level2_act]
missing_l2_act <- setdiff(present_level2_act, names(level2_cols_act))
if (length(missing_l2_act) > 0) {
level2_cols_act <- c(level2_cols_act, setNames(rep("grey80", length(missing_l2_act)), missing_l2_act))
}
ann_colors_act <- list(Level1 = level1_cols_act, Level2 = level2_cols_act)
rle_l1_act <- rle(as.character(ann_col_act$Level1))
gaps_col_act <- cumsum(rle_l1_act$lengths)
gaps_col_act <- gaps_col_act[-length(gaps_col_act)]
pheatmap(
M_fc_act_cap,
cluster_rows = FALSE, cluster_cols = FALSE,
gaps_row = gaps_row, gaps_col = gaps_col_act,
color = colorRampPalette(c("#7A0177", "black", "#FFD700"))(101),
breaks = seq(-cap, cap, length.out = 102),
show_colnames = FALSE, fontsize_row = 7, fontface_row = "bold", border_color = NA,
annotation_col = ann_col_act, annotation_colors = ann_colors_act, annotation_names_col = TRUE,
useRaster = TRUE,
main = "GP Log2FC vs per-lineage RESTING baseline (CD4 cells vs CD4 resting; CD8 vs CD8 resting)",
filename = paste0(figure_path, "s3e.pdf"),
width = 12, height = 4
)
Extended Data Fig. 3e. Heatmap showing single-cell GP activity in activated CD4+ and CD8+ T cells: per-cell log2 fold change in GP activity relative to the resting baseline of the same lineage (each activated CD4 cell versus the CD4 resting mean; each activated CD8 cell versus the CD8 resting mean), capped at +-2. Rows are the activation-associated GPs in semantic-group order; columns are individual activated cells grouped by cluster (Level-2 sub-lineage) within CD4 and CD8; color runs from purple (low) through black to gold (high).
(f) GP57+ rate, cancer vs. other conditions
# ============================================================
# s3f: GP57 high-loading proportion, cancer vs other conditions
# (activated CD4 and CD8)
# ============================================================
gp57_condition_df <- data.frame(
lineage = seurat_meta_filtered$annotation_level1,
activation_group = seurat_meta_filtered$annotation_level2_group,
condition_broad = as.character(seurat_meta_filtered$condition_broad),
gp57_loading = L_pm_filtered[, "GP57"],
stringsAsFactors = FALSE
) %>%
filter(lineage %in% c("CD8", "CD4"), activation_group == "activated") %>%
mutate(
lineage = factor(lineage, levels = c("CD8", "CD4")),
condition_group = if_else(condition_broad == "cancer", "Cancer", "Other conditions"),
condition_group = factor(condition_group, levels = c("Cancer", "Other conditions")),
gp57_high = gp57_loading > 0.1
)
gp57_condition_summary <- gp57_condition_df %>%
group_by(lineage, condition_group) %>%
summarise(n_cells = n(), n_gp57_high = sum(gp57_high), proportion_gp57_high = mean(gp57_high), .groups = "drop")
gp57_ymax <- max(gp57_condition_summary$proportion_gp57_high, na.rm = TRUE)
gp57_ymax <- ifelse(gp57_ymax > 0, gp57_ymax * 1.25, 0.05)
p_s3f <- ggplot(gp57_condition_summary, aes(x = lineage, y = proportion_gp57_high, fill = condition_group)) +
geom_col(position = position_dodge(width = 0.72), width = 0.62, color = "grey20", linewidth = 0.25) +
geom_text(
aes(label = paste0(scales::percent(proportion_gp57_high, accuracy = 0.1), "\n", n_gp57_high, "/", n_cells)),
position = position_dodge(width = 0.72), vjust = -0.25, size = 3.4, lineheight = 0.9
) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits = c(0, gp57_ymax), expand = expansion(mult = c(0, 0.04))) +
scale_fill_manual(values = c("Cancer" = "#C44E52", "Other conditions" = "#4C72B0")) +
labs(x = NULL, y = "Proportion of activated cells", fill = "Condition", title = "GP57 loading > 0.1 in activated CD8 and CD4 cells") +
theme_classic(base_size = 12) +
theme(legend.position = "top", legend.title = element_text(face = "bold"), axis.text.x = element_text(face = "bold"), plot.title = element_text(face = "bold", hjust = 0.5))
ggsave(filename = paste0(figure_path, "s3f.pdf"), plot = p_s3f, width = 5.4, height = 4.2)
Extended Data Fig. 3f. Bar plot showing the percentage of GP57-active cells (GP57 activity above 0.1) among activated CD8+ and CD4+ T cells in cancer versus all other conditions combined; bar labels give the percentage and the underlying cell counts.
(g) TF-GP network for Gata3/Rorc/Tbx21
# ============================================================
# s3g: Bipartite TF-GP network for Gata3, Rorc, Tbx21
# ============================================================
tf_focus <- c("Gata3", "Rorc", "Tbx21")
tf_focus_threshold <- 0.1
tf_focus_edges <- do.call(rbind, lapply(tf_focus, function(tf_name) {
vals <- setNames(as.numeric(F_pm_filtered_norm[tf_name, ]), colnames(F_pm_filtered_norm))
idx <- which(is.finite(vals) & abs(vals) >= tf_focus_threshold)
data.frame(TF = tf_name, GP = names(vals)[idx], value = vals[idx], stringsAsFactors = FALSE)
})) %>%
mutate(
GP_number = as.numeric(sub("^GP", "", GP)),
edge_sign = if_else(value < 0, "Negative", "Positive"),
abs_value = abs(value),
edge_label = sprintf("%+.2f", value)
) %>%
arrange(match(TF, tf_focus), GP_number)
tf_focus_nodes <- data.frame(name = tf_focus, x = c(0, 2.7, 1.65), y = c(0, -2.75, 2.35), node_type = "TF", stringsAsFactors = FALSE)
tf_focus_colors <- c(Gata3 = "#E41A1C", Rorc = "#1F78B4", Tbx21 = "#33A02C")
gp_tf_membership <- tf_focus_edges %>%
group_by(GP) %>%
summarise(tf_members = list(sort(unique(TF))), degree_tf = n_distinct(TF), .groups = "drop")
shared_gp_nodes <- gp_tf_membership %>%
filter(degree_tf > 1) %>%
rowwise() %>%
mutate(
x = mean(tf_focus_nodes$x[match(tf_members, tf_focus_nodes$name)]),
y = mean(tf_focus_nodes$y[match(tf_members, tf_focus_nodes$name)])
) %>%
ungroup() %>%
transmute(name = GP, x, y, node_type = "GP")
tf_arc_range <- list(Gata3 = c(170, -105), Rorc = c(205, -20), Tbx21 = c(165, -15))
tf_arc_radius <- c(Gata3 = 1.65, Rorc = 1.35, Tbx21 = 1.30)
make_tf_arc_nodes <- function(tf_name, gp_names) {
if (length(gp_names) == 0) return(NULL)
center <- tf_focus_nodes[tf_focus_nodes$name == tf_name, ]
angles <- seq(tf_arc_range[[tf_name]][1], tf_arc_range[[tf_name]][2], length.out = length(gp_names)) * pi / 180
radius <- tf_arc_radius[[tf_name]]
data.frame(name = gp_names, x = center$x + radius * cos(angles), y = center$y + radius * sin(angles), node_type = "GP", stringsAsFactors = FALSE)
}
exclusive_gp_nodes <- do.call(rbind, lapply(tf_focus, function(tf_name) {
gp_names <- gp_tf_membership %>%
filter(degree_tf == 1, vapply(tf_members, identical, logical(1), tf_name)) %>%
pull(GP)
gp_names <- gp_names[order(as.numeric(sub("^GP", "", gp_names)))]
make_tf_arc_nodes(tf_name, gp_names)
}))
tf_focus_plot_nodes <- bind_rows(tf_focus_nodes, shared_gp_nodes, exclusive_gp_nodes)
tf_focus_plot_edges <- tf_focus_edges %>%
left_join(tf_focus_plot_nodes %>% select(name, x, y) %>% rename(x0 = x, y0 = y), by = c("TF" = "name")) %>%
left_join(tf_focus_plot_nodes %>% select(name, x, y) %>% rename(x1 = x, y1 = y), by = c("GP" = "name")) %>%
mutate(
label_x = x0 + 0.55 * (x1 - x0), label_y = y0 + 0.55 * (y1 - y0),
label_angle = atan2(y1 - y0, x1 - x0) * 180 / pi,
label_angle = case_when(label_angle > 90 ~ label_angle - 180, label_angle < -90 ~ label_angle + 180, TRUE ~ label_angle)
)
p_s3g <- ggplot() +
geom_segment(data = tf_focus_plot_edges, aes(x = x0, y = y0, xend = x1, yend = y1, color = edge_sign, linewidth = abs_value), alpha = 0.65, lineend = "round") +
geom_text(data = tf_focus_plot_edges, aes(x = label_x, y = label_y, label = edge_label, angle = label_angle), size = 3.0, color = "grey20") +
geom_point(data = tf_focus_plot_nodes %>% filter(node_type == "GP"), aes(x = x, y = y), shape = 21, size = 4.0, fill = "grey78", color = "grey45", stroke = 0.5) +
ggrepel::geom_text_repel(
seed = 42,
data = tf_focus_plot_nodes %>% filter(node_type == "GP"), aes(x = x, y = y, label = name),
size = 3.4, color = "grey20", max.overlaps = Inf, min.segment.length = Inf, box.padding = 0.2, point.padding = 0.25
) +
geom_label(
data = tf_focus_plot_nodes %>% filter(node_type == "TF"), aes(x = x, y = y, label = name, fill = name),
color = "white", fontface = "bold", size = 4.6, label.padding = unit(0.22, "lines"), label.r = unit(0.16, "lines"), linewidth = 0, show.legend = FALSE
) +
scale_color_manual(values = c(Negative = "#2B6CB0", Positive = "#D62728"), name = "Edge sign") +
scale_fill_manual(values = tf_focus_colors) +
scale_linewidth_continuous(range = c(0.45, 3.2), guide = "none") +
coord_equal(clip = "off") +
labs(title = paste0("TF <-> GP network (", length(tf_focus), " TFs, ", nrow(tf_focus_edges), " edges)")) +
theme_void(base_size = 12) +
theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15), legend.position = "right", legend.title = element_text(face = "bold"), plot.margin = margin(15, 25, 15, 25))
ggsave(filename = paste0(figure_path, "s3g.pdf"), plot = p_s3g, width = 10, height = 8.5)
Extended Data Fig. 3g. GP network centered on the lineage-defining TFs Gata3, Rorc, and Tbx21. An edge connects a TF to a GP when the TF’s per-GP absolute gene score is >= 0.1; edges are colored by the sign of the score (red, up-regulated; blue, down-regulated), edge width is proportional to the score, and labels give the signed score.
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