Last updated: 2026-09-02

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Knit directory: immgenT-GP-analysis/analysis/

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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")

# A figure script here was once seen to exit 0 with a complete log and write
# nothing at all (see script/README.md, "A re-run can silently not write"), so
# record when this run started and 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).
# Those three panels are in two different figures and so in two different
# scripts, but they must show the same GPs on the same axis order -- hence one
# definition here rather than a copy in each script.
#
# Curated list -- NOT a top-10 computed from the correlations below. These 10
# are drawn from among the most strongly CD69-correlated GPs, but are not the
# top 10 under any single ranking: 8 are positively correlated (ranks 1, 3, 4,
# 5, 6, 10, 12, 14 of 200) and GP58/GP171 are the two most *negatively*
# correlated GPs of all 200. By |rho| they sit at ranks 1, 2, 4, 5, 6, 8, 12,
# 14, 17, 18, skipping GP1/GP47/GP100/GP25. Treat as a hand-picked input like
# Thresholds_Selected_Proteins.csv and well_aligned_gps -- don't "fix" it into
# a computed ranking, and keep the captions' "from among the most associated"
# wording in sync (analysis/Figure6.Rmd Fig. 6d, analysis/FigureS7.Rmd
# Fig. S7a, b). script/verify_cd69_gp_ranking.R enforces all of this.
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)

Extended Data Fig. 7a, b. Mean activity of the ten CD69-associated GPs of Fig. 6d, in the same order, (a) across tissues (organ_simplified) and (b) across lineages (annotation_level1). That order is the Spearman correlation between GP activity and CD69 protein expression. Color runs from white (zero) to firebrick (each panel’s largest mean activity). The ten GPs are a hand-picked set drawn from among the GPs most strongly correlated – positively or negatively – with CD69, not a computed top-10; script/verify_cd69_gp_ranking.R recomputes the correlation over all 200 GPs and fails if this wording and the code drift apart.

(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")
  )
}

Extended Data Fig. 7c-f. Examples of gating strategies used to identify GP-active cells, as in Fig. 6e-j, for (c) GP29, (d) GP58, (e) GP22 and (f) GP68. For each GP, all-T MDE plots highlight cells passing a protein gate built from the GP’s curated marker signature (left; positive markers above threshold, negative markers at or below, with the gate size reported in the panel title as “Matched n”) and, on the right, an equally sized set of cells with the highest GP activity – exactly as many cells as the protein gate selected. Color indicates cell density (two-dimensional); all other cells are grey. Thymocytes, proliferating, and “miniverse” cells are excluded. Each panel is labeled with its GP and the markers the gate actually applied; every marker in these four signatures has a manually reviewed positivity threshold, so none is silently dropped.


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