Last updated: 2026-09-10

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File Version Author Date Message
html 59021bf Ziang Zhang 2026-09-10 Build site: Extended Data 1-6 after removing the structure-plot figure
Rmd c21f51f Ziang Zhang 2026-09-10 Remove the per-lineage structure-plot figure; keep its record for Figure 4
html 60125af Ziang Zhang 2026-09-10 Build site: Figure 4b on the shared column colours
html 88ee847 Ziang Zhang 2026-09-10 Build site: Figure 4b without the miniverse clusters
html 5874416 Ziang Zhang 2026-09-10 Build site: main Figure 4 inserted, Extended Data back to 1-7
Rmd 4307b28 Ziang Zhang 2026-09-10 New main Figure 4, and fold the cluster heatmap into Extended Data Figure 2
html a7a481f Ziang Zhang 2026-09-09 Build site: the rebuilt Figure 1d and the Extended Data renumbering
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
html dd76d38 Ziang Zhang 2026-08-19 Build site: Figure 7 and index pages rebuilt
Rmd 801dbf7 Ziang Zhang 2026-08-19 Figure 7 pages: present Figure 7 as one figure spanning EBMF and RQVI
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 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

All panels except (a) are produced by script/Figure7.R, which shares its CITE-seq setup with Extended Data Figure 5 and Extended Data Figure 5 via code/R/citeseq_shared_setup.R. The code below is shown for reference (not re-executed on this page, since this script takes about a minute to run); the images are its pre-rendered output.

Setup

library(ggplot2)
library(ggrepel)
library(dplyr)
library(patchwork)
library(tidyr)
library(Matrix) # protein matrices are dgCMatrix; must be attached for `[` to dispatch

data_path <- "data/"
figure_path <- "figures/final-selected/Figure 7/"
source("code/R/gated_protein_helpers.R")

# 7a: hand-drawn schematic -- not code-generated, no output here.

Panels (b) onward additionally load the shared CITE-seq cell/protein setup:

# ============================================================
# Load data (shared with FigureS4.R and FigureS5.R)
# ============================================================
source("code/R/citeseq_shared_setup.R")

(a) Protein-signature schematic

This panel is a hand-drawn schematic, not generated from code.

How the protein factor matrix is estimated is documented in Methods: fitting CITE-seq protein programs with fixed scRNA loadings.

Fig. 7a. Schematic illustrating how protein signatures were defined for each GP. Holding the cell-loading matrix L fixed from the GP model, a protein factor matrix was estimated by EBMF from the paired CITE-seq protein measurements, Y ≈ LUᵀ (cells × proteins matrix), yielding a protein signature U (proteins × GPs matrix) for each GP.

(b, c) KLRG1 modulation across lineages

# ============================================================
# 7b/7c: KLRG1 modulation (CD8 vs CD4, CD8 vs Treg)
# ============================================================
FlashierDGE_corrected <- function(F1, L1, group1, group2, title_plot = "") {
  loadings_group1 <- colMeans(L1[group1, ])
  loadings_group2 <- colMeans(L1[group2, ])
  mean_change_loadings <- loadings_group1 - loadings_group2
  vplot <- data.frame(SYMBOL = names(mean_change_loadings), mean_change_loadings = mean_change_loadings, AveExpr = colMeans(L1[c(group1, group2), ]))
  list(diff_factors = vplot)
}
get_klrg1_split <- function(cell_type_label, meta, protein_data, threshold) {
  cells <- meta$cellID[meta$annotation_level1 == cell_type_label]
  cells <- intersect(cells, rownames(protein_data))
  list(pos = cells[protein_data[cells, "KLRG1"] >= threshold], neg = cells[protein_data[cells, "KLRG1"] < threshold])
}
run_checked_dge <- function(group_list, F_mat, L_mat, label) {
  if (length(group_list$pos) < 3 || length(group_list$neg) < 3) stop(paste("Insufficient data:", label))
  df <- FlashierDGE_corrected(F1 = F_mat, L1 = L_mat, group1 = group_list$pos, group2 = group_list$neg)$diff_factors
  if (!"SYMBOL" %in% colnames(df)) df$SYMBOL <- rownames(df)
  df
}
plot_target_gps <- function(df, x_var, y_var, label_var, target_gps, highlight_color = "darkorange", background_color = "black",
                             x_limits = c(-0.5, 0.5), y_limits = c(-0.5, 0.5), background_alpha = 0.5,
                             xlab = "Difference in Mean Loading", ylab = "Difference in Mean Loading", title = "Comparison of Specific GP Loadings") {
  highlight_df <- df %>% filter({{ label_var }} %in% target_gps) %>% mutate(.label_display = as.character({{ label_var }}))
  ggplot(df, aes(x = {{ x_var }}, y = {{ y_var }})) +
    geom_point(color = background_color, alpha = background_alpha) +
    geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "red") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "blue") +
    geom_vline(xintercept = 0, linetype = "dashed", color = "blue") +
    geom_point(data = highlight_df, aes(color = {{ label_var }}), size = 2) +
    ggrepel::geom_text_repel(seed = 42, data = highlight_df, aes(label = .label_display, color = {{ label_var }}), max.overlaps = Inf, size = 3.5, box.padding = 0.35, point.padding = 0.5, segment.color = "grey50", show.legend = FALSE) +
    scale_color_manual(values = highlight_color, guide = "none") +
    coord_cartesian(xlim = x_limits, ylim = y_limits) +
    labs(x = xlab, y = ylab, title = title) +
    theme_minimal()
}

klrg1_threshold <- threshold_results_subset_manual$Threshold[threshold_results_subset_manual$Protein == "KLRG1"]
cd8_split <- get_klrg1_split("CD8", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)
CD4_split <- get_klrg1_split("CD4", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)
treg_split <- get_klrg1_split("Treg", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)

diff_CD8 <- run_checked_dge(cd8_split, F_pm_filtered, L_pm_filtered, "CD8") %>% rename(mean_change_CD8 = mean_change_loadings, AveExpr_CD8 = AveExpr)
diff_CD4 <- run_checked_dge(CD4_split, F_pm_filtered, L_pm_filtered, "CD4") %>% rename(mean_change_CD4 = mean_change_loadings, AveExpr_CD4 = AveExpr)
diff_Treg <- run_checked_dge(treg_split, F_pm_filtered, L_pm_filtered, "Treg") %>% rename(mean_change_Treg = mean_change_loadings, AveExpr_Treg = AveExpr)

# 7b: CD8 vs CD4
merged_cd4 <- inner_join(diff_CD4, diff_CD8, by = "SYMBOL")
p_7b <- plot_target_gps(
  df = merged_cd4, x_var = mean_change_CD8, y_var = mean_change_CD4, label_var = SYMBOL,
  target_gps = c("GP10", "GP58", "GP25", "GP26", "GP43"), background_alpha = 0.8, x_limits = c(-0.2, 0.4), y_limits = c(-0.2, 0.4),
  highlight_color = c("GP10" = "darkorange2", "GP25" = "blue", "GP43" = "blue", "GP26" = "blue", "GP58" = "darkorange2"),
  title = "KLRG1 Modulation: CD8 vs CD4", xlab = "Effect Size in CD8 (KLRG1+ - KLRG1-)", ylab = "Effect Size in CD4 (KLRG1+ - KLRG1-)"
) + theme_bw()
ggsave(paste0(figure_path, "7b.pdf"), p_7b, width = 7, height = 6)

# 7c: CD8 vs Treg
merged_treg <- inner_join(diff_Treg, diff_CD8, by = "SYMBOL")
p_7c <- plot_target_gps(
  df = merged_treg, x_var = mean_change_CD8, y_var = mean_change_Treg, label_var = SYMBOL,
  target_gps = c("GP6", "GP10", "GP12", "GP27", "GP68", "GP58"), background_alpha = 0.8, x_limits = c(-0.2, 0.4), y_limits = c(-0.2, 0.4),
  highlight_color = c("GP10" = "darkorange2", "GP27" = "deeppink", "GP6" = "deeppink", "GP68" = "deeppink", "GP12" = "deeppink", "GP58" = "darkorange2"),
  title = "KLRG1 Modulation: CD8 vs Treg", xlab = "Effect Size in CD8 (KLRG1+ - KLRG1-)", ylab = "Effect Size in Treg (KLRG1+ - KLRG1-)"
) + theme_bw()
ggsave(paste0(figure_path, "7c.pdf"), p_7c, width = 7, height = 6)

Version Author Date
5874416 Ziang Zhang 2026-09-10

Fig. 7b, c. GPs associated with KLRG1 protein expression in CD4, Tregs, and CD8. Effect-size versus effect-size plots, where each GP’s effect size is the difference in its mean activity between KLRG1+ and KLRG1- cells, comparing CD8+ T cells (x-axis) with CD4+ T cells (b) or Treg cells (c).

(d) Genes up- and down-regulated across the CD69-associated GPs

The ten GPs, and the correlation-based order they are drawn in, are defined once in code/R/citeseq_shared_setup.R, because Extended Data Fig. 5a, b show the same GPs in the same order from a different script:

# The CD69-associated GP subset, shared by Figure 7d (the up/down gene heatmap)
# and Figure S5a/S5b (the same GPs' mean activity per tissue and per lineage).
# Defined once here because those panels live in two different scripts and must
# show the same GPs in the same axis order. Curated, not a computed top-10.
cd69_top_gps_subset <- c("GP35", "GP6", "GP170", "GP26", "GP58", "GP171", "GP63", "GP62", "GP3", "GP29")
shared_cells_cd69 <- intersect(rownames(L_pm_filtered), rownames(protein_mat_normalized_lognorm))
cd69_expr_vec <- protein_mat_normalized_lognorm[shared_cells_cd69, "CD69"]
cd69_corr <- sapply(cd69_top_gps_subset, function(gp) cor(L_pm_filtered[shared_cells_cd69, gp], cd69_expr_vec, method = "spearman"))
# most-correlated GP ends up at the top of the y-axis in all three panels
cd69_top_gps_sorted <- names(sort(cd69_corr, decreasing = FALSE))
# ============================================================
# 7d: up/down genes across the 10 curated CD69-associated GPs
# (cd69_top_gps_subset / cd69_corr / cd69_top_gps_sorted come from
# citeseq_shared_setup.R, which Figure S5's s5a/s5b panels share)
# ============================================================
D_scale6 <- diag(1 / apply(F_pm_filtered, 2, function(x) max(abs(x), na.rm = TRUE)))
F_pm_filtered_scaled <- F_pm_filtered %*% D_scale6
colnames(F_pm_filtered_scaled) <- paste0("GP", 1:ncol(F_pm_filtered_scaled))

plot_factor_heatmap <- function(F_matrix, gp_vector, n_top = 5, min_abs_loading = 0.5, transpose = FALSE,
                                 title = "Factor loadings – top genes per GP", low_color = "steelblue", mid_color = "white", high_color = "firebrick", font_size = 9) {
  F_sub <- F_matrix[, gp_vector, drop = FALSE]
  selected_genes <- lapply(gp_vector, function(gp) {
    vals <- F_sub[, gp]
    top_pos <- names(sort(vals, decreasing = TRUE))[seq_len(min(n_top, sum(vals > 0)))]
    top_neg <- names(sort(vals, decreasing = FALSE))[seq_len(min(n_top, sum(vals < 0)))]
    c(top_pos, top_neg)
  })
  selected_genes <- unique(unlist(selected_genes))
  if (min_abs_loading > 0) {
    max_abs <- apply(F_sub[selected_genes, , drop = FALSE], 1, function(x) max(abs(x), na.rm = TRUE))
    selected_genes <- names(max_abs[max_abs >= min_abs_loading])
  }
  hc_genes <- hclust(dist(F_sub[selected_genes, , drop = FALSE]))
  gene_order <- rownames(F_sub[selected_genes, , drop = FALSE])[hc_genes$order]
  plot_df <- F_sub[selected_genes, , drop = FALSE] %>%
    as.data.frame() %>%
    tibble::rownames_to_column("Gene") %>%
    tidyr::pivot_longer(cols = -Gene, names_to = "GP", values_to = "Loading") %>%
    mutate(GP = factor(GP, levels = gp_vector), Gene = factor(Gene, levels = gene_order))
  limit <- max(abs(plot_df$Loading), na.rm = TRUE)
  x_aes <- if (transpose) "Gene" else "GP"
  y_aes <- if (transpose) "GP" else "Gene"
  ggplot(plot_df, aes(x = .data[[x_aes]], y = .data[[y_aes]], fill = Loading)) +
    geom_tile() +
    scale_fill_gradient2(low = low_color, mid = mid_color, high = high_color, midpoint = 0, limits = c(-limit, limit), name = "Loading") +
    # Axis titles are the faceting variables themselves ("Gene" / "GP").
    labs(title = title, x = x_aes, y = y_aes) +
    theme_minimal(base_size = font_size) +
    theme(axis.text.x = element_text(angle = if (transpose) 90 else 45, hjust = 1, size = font_size), axis.text.y = element_text(size = font_size), panel.grid = element_blank(), plot.title = element_text(face = "bold"))
}

p_heatmap <- plot_factor_heatmap(F_matrix = F_pm_filtered_scaled, gp_vector = cd69_top_gps_sorted, n_top = 5, font_size = 9, transpose = TRUE, low_color = "#4DAF4A", mid_color = "white", high_color = "#984EA3")

corr_strip_df <- data.frame(GP = factor(cd69_top_gps_sorted, levels = cd69_top_gps_sorted), Correlation = cd69_corr[cd69_top_gps_sorted], x = "Corr")
corr_limit <- max(abs(corr_strip_df$Correlation))
p_corr_strip <- ggplot(corr_strip_df, aes(x = x, y = GP, fill = Correlation)) +
  geom_tile() +
  scale_fill_gradient2(low = "royalblue", mid = "white", high = "tomato", midpoint = 0, limits = c(-corr_limit, corr_limit), name = "Corr\n(CD69)") +
  labs(x = NULL, y = NULL) +
  theme_minimal(base_size = 9) +
  theme(axis.text.x = element_text(size = 9, angle = 45, hjust = 1), axis.text.y = element_blank(), axis.ticks.y = element_blank(), panel.grid = element_blank())

p_7d <- p_corr_strip + p_heatmap + patchwork::plot_layout(widths = c(0.06, 1), guides = "collect")
ggsave(paste0(figure_path, "7d.pdf"), p_7d, width = 11, height = 5)

Version Author Date
5874416 Ziang Zhang 2026-09-10

Fig. 7d. Heatmap of the top five most strongly up- and down-regulated gene scores for ten GPs correlated with CD69 expression, with the left strip indicating the Spearman correlation between GP activity and CD69 expression.

(e-j) Protein-gated vs. GP-loading populations

# ============================================================
# 7e-7j: protein-gate vs. GP-loading comparison for the 6 curated main-figure
# GPs (df_markers2, thymocyte/proliferating/miniverse_cells, L_pm_for_gating,
# select_proteins, threshold_results_subset_manual, enlarge_gps all come from
# citeseq_shared_setup.R above)
# ============================================================
# Panel lettering is carried by this named vector and the loop iterates over its
# names, so a GP can never be drawn under another GP's letter.
fig7_gating <- c("GP171" = "7e", "GP12" = "7f", "GP80" = "7g", "GP23" = "7h", "GP77" = "7i", "GP8" = "7j")
for (gp in names(fig7_gating)) {
  k_name <- paste0("K", sub("^GP", "", gp))
  plot_gated_gp_vs_protein(
    gp_name = k_name,
    df_markers = df_markers2,
    protein_mat = protein_mat_normalized_lognorm,
    loading_mat = L_pm_for_gating,
    mde_emb = mde_result,
    missing_threshold_action = "skip",
    threshold_df = threshold_results_subset_manual,
    exclude_cells = c(thymocyte_cells, proliferating_cells, miniverse_cells),
    selected_proteins = select_proteins,
    loading_q = NULL,
    min_pointsize = if (gp %in% enlarge_gps) 3L else 0L,
    save_path = paste0(figure_path, fig7_gating[gp], ".pdf")
  )
}

Version Author Date
5874416 Ziang Zhang 2026-09-10

Version Author Date
5874416 Ziang Zhang 2026-09-10

Version Author Date
5874416 Ziang Zhang 2026-09-10

Version Author Date
5874416 Ziang Zhang 2026-09-10

Version Author Date
5874416 Ziang Zhang 2026-09-10

Version Author Date
5874416 Ziang Zhang 2026-09-10

Fig. 7e-j. Examples of gating strategies used to identify GP-active cells. All-T MDE plots highlight cells selected by the proposed gating strategy (left) and the corresponding GP-active cells (right). Color indicates cell density.


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