Figure S3: Characterizing activation-associated GPs
Ziang Zhang
Last updated: 2026-07-28
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immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 |
Published as Extended Data Figure 3.
Panels (s3c)-(s3h) 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.
Panels (s3a) and (s3b) are not produced in this repository – see below.
Setup
# Figure S3. Characterizing activation GPs.
#
# Panels produced (see figures/Previous/bits/Figure S3/FigureS3_caption.md
# for the full caption text; captioned A-F there but the final files are
# lettered s3c-s3h -- this script uses that final lettering):
# s3c Fraction of activated CD4/CD8 cells per organ with GP26 > 0.1, sorted.
# s3d GSEA dot plot relating the activation GPs to curated gene sets.
# s3e Fraction of activated CD4 cells with GP79 loading > 0.1, by condition.
# s3f Per-cell log2FC heatmap of activation GPs vs resting baseline.
# s3g Fraction of activated CD8/CD4 cells with GP57 loading > 0.1: cancer
# vs all other conditions.
# s3h Bipartite TF-GP network for Gata3, Rorc, Tbx21.
#
# Source: ported from Figure_Activation.R (see Figure4.R for the main
# Figure 4 panels from the same file). Shared curated-GP setup lives in
# code/R/activation_shared_setup.R.
#
# Panel s3c 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. This exactly reproduces the collaborator's panel
# (all 22 organ rates match to the decimal); see the s3c block below.
#
# 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()
source("code/R/tf_network.R") # optimize_bipartite_order(), plot_tf_gp_network_v2()
# ============================================================
# 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")(s3a, s3b) Definition of resting and activated CD4/CD8 cells
These two panels are missing from this site: no code
in script/FigureS3.R produces them, and there is no
s3a/s3b asset under
analysis/assets/FigureS3/,
figures/final-selected/Figure S3/, or
figures/Previous/bits/. The published captions describe
them as follows.
Fig. S3a. 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.
Fig. S3b. 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 these two
panels define, so the gate itself is already in use here – only the two
figures illustrating it are absent.
(s3c) GP26+ rate by organ
# ============================================================
# s3c: 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_s3c <- 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, "s3c.pdf"), plot = p_s3c, width = 9, height = 4.8)
Fig. S3c. 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.
(s3d) GSEA dot plot
# ============================================================
# s3d: 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_s3d <- 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, "s3d.pdf"), plot = p_s3d, width = 8, height = 10)
Fig. S3d. 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.
(s3e) GP79+ rate across conditions
# ============================================================
# s3e: 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_s3e <- 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, "s3e.pdf"), plot = p_s3e, width = 9, height = gp79_plot_height, limitsize = FALSE)
Fig. S3e. 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.
(s3f) Per-cell log2FC heatmap
# ============================================================
# s3f: 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, "s3f.pdf"),
width = 12, height = 4
)
Fig. S3f. 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).
(s3g) GP57+ rate, cancer vs. other conditions
# ============================================================
# s3g: 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_s3g <- 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, "s3g.pdf"), plot = p_s3g, width = 5.4, height = 4.2)
Fig. S3g. 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.
(s3h) TF-GP network for Gata3/Rorc/Tbx21
# ============================================================
# s3h: 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_s3h <- 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, "s3h.pdf"), plot = p_s3h, width = 10, height = 8.5)
Fig. S3h. 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