Last updated: 2026-07-26

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Knit directory: immgenT-GP-analysis/analysis/

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File Version Author Date Message
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

All panels 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.

Setup

# Figure S3. Characterizing activation GPs.
#
# Panels produced (see figures/final-selected/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/generated/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")

(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)

Version Author Date
fa5a501 Ziang Zhang 2026-07-18
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. S3c. Bar plot of the fraction of activated CD4/CD8 cells in each organ (organ_simplified) with GP26 loading 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)

Version Author Date
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. S3d. GSEA dot plot relating the activation GPs to curated immunologic signature gene sets. Each dot is a significant GP-gene-set association; color denotes -log10 adjusted p-value and 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)

Version Author Date
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. S3e. Bar plot of the fraction of activated CD4 cells with GP79 loading above 0.1, across 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
)

Version Author Date
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. S3f. Heatmap of per-cell log2 fold-change in GP loading 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 GPs in semantic-group order; columns are individual activated cells grouped by sub-lineage (Level-2) 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)

Version Author Date
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. S3g. Fraction of activated CD8 and CD4 cells with GP57 loading above 0.1, comparing cancer conditions against 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(
    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)

Version Author Date
78e3bba Ziang Zhang 2026-07-03
06b2461 Ziang Zhang 2026-07-02

Fig. S3h. Bipartite transcription factor (TF)-GP network for the lineage-defining TFs Gata3, Rorc, and Tbx21. An edge connects a TF to a GP when the TF’s per-GP-normalized gene score has magnitude >= 0.1; edge color denotes sign (red, positive; blue, negative), width scales with magnitude, 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