Last updated: 2026-09-04

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Knit directory: immgenT-GP-analysis/analysis/

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File Version Author Date Message
Rmd 0267e5b Ziang Zhang 2026-09-04 Captions from the published manuscript; trim editor notes off the page code
html 19c977f Ziang Zhang 2026-09-02 Build site: Extended Data 5-7 renumbered, Figure S5 page rebuilt
Rmd 1e5721a Ziang Zhang 2026-09-02 Extended Data 5-7 renumbered, and Figure S5 assembled as one stacked figure

All panels are produced by script/FigureS7.R, which shares its CITE-seq setup, gating logic and CD69 GP subset with Figure 6 via code/R/citeseq_shared_setup.R and code/R/gated_protein_helpers.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(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 S7/"
source("code/R/gated_protein_helpers.R")
source("code/R/citeseq_shared_setup.R")

# Record when this run started, to assert at the end that every panel is newer.
run_started_at <- Sys.time()

(a, b) CD69-associated GPs across tissues and lineages

The ten GPs and their order are defined in code/R/citeseq_shared_setup.R, so that these two panels and Fig. 6d – which live in different figures and therefore in different scripts – cannot disagree about which GPs they show or in what order:

# The CD69-associated GP subset, shared by Figure 6d (the up/down gene heatmap)
# and Figure S7a/S7b (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))
# ============================================================
# s7a/s7b: mean loading of the 10 curated CD69-associated GPs, per tissue (a)
# and per lineage (b). cd69_top_gps_sorted comes from citeseq_shared_setup.R
# and is the same GP order Figure 6d draws.
# ============================================================
cells_for_heatmap <- intersect(rownames(L_pm_filtered), rownames(seurat_meta_filtered))
L_cd69_sub <- L_pm_filtered[cells_for_heatmap, cd69_top_gps_sorted, drop = FALSE]
meta_hm <- seurat_meta_filtered[cells_for_heatmap, c("annotation_level1", "organ_simplified")]

mean_loading_long <- function(L_mat, group_vec, gp_levels) {
  as.data.frame(L_mat) %>%
    mutate(group = group_vec) %>%
    tidyr::pivot_longer(cols = -group, names_to = "GP", values_to = "Loading") %>%
    group_by(group, GP) %>%
    summarise(mean_loading = mean(Loading, na.rm = TRUE), .groups = "drop") %>%
    mutate(GP = factor(GP, levels = gp_levels))
}
make_mean_loading_heatmap <- function(df, title) {
  fill_max <- max(df$mean_loading, na.rm = TRUE)
  ggplot(df, aes(x = group, y = GP, fill = mean_loading)) +
    geom_tile() +
    scale_fill_gradient(low = "white", high = "firebrick", limits = c(0, fill_max), name = "Mean\nloading") +
    labs(title = title, x = NULL, y = NULL) +
    theme_minimal(base_size = 9) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9), axis.text.y = element_text(size = 9), panel.grid = element_blank())
}

df_organ <- mean_loading_long(L_cd69_sub, meta_hm$organ_simplified, cd69_top_gps_sorted)
p_s7a <- make_mean_loading_heatmap(df_organ, "Mean GP loading by tissue (organ_simplified)")
ggsave(paste0(figure_path, "s7a.pdf"), p_s7a, width = 9, height = 5)

df_level1 <- mean_loading_long(L_cd69_sub, meta_hm$annotation_level1, cd69_top_gps_sorted)
p_s7b <- make_mean_loading_heatmap(df_level1, "Mean GP loading by cell type (level1)")
ggsave(paste0(figure_path, "s7b.pdf"), p_s7b, width = 7, height = 5)

Version Author Date
19c977f Ziang Zhang 2026-09-02

Version Author Date
19c977f Ziang Zhang 2026-09-02

Extended Data Fig. 7a, b. Mean activity of the ten CD69-associated GPs (a) across tissues and (b) across lineages.

(c-f) Further protein-gated vs. GP-loading populations

# ============================================================
# s7c-s7f: protein-gate vs. GP-loading comparison for the 4 curated
# supplementary GPs. Same helper, same inputs and same panel geometry as
# Figure 6e-6j -- only the GPs differ, and the two sets are disjoint.
# ============================================================
# As in Figure6.R, the loop iterates over the names of the letter map so a GP
# cannot be drawn under another GP's letter.
figs7_gating <- c("GP29" = "s7c", "GP58" = "s7d", "GP22" = "s7e", "GP68" = "s7f")
for (gp in names(figs7_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, figs7_gating[gp], ".pdf")
  )
}

Version Author Date
19c977f Ziang Zhang 2026-09-02

Version Author Date
19c977f Ziang Zhang 2026-09-02

Version Author Date
19c977f Ziang Zhang 2026-09-02

Version Author Date
19c977f Ziang Zhang 2026-09-02

Extended Data Fig. 7c-f. Examples of gating strategies used to identify GP-active cells for (c) GP29 (CD8aa gdT or ab T cell specific), (d) GP58 (CD8-specific), (e) GP22 (DN-specific), and (f) GP68 (Treg-specific).


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