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File Version Author Date Message
Rmd 5651d0e Ziang Zhang 2026-08-05 Extended Data tables: reorder to six, rebuild Table 1, drop internal notes
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
Rmd c9b020f Ziang Zhang 2026-07-30 Split Figure S6’s protein-program heatmap out as Figure S5
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
html 029b0ae Ziang Zhang 2026-07-28 Build site.
Rmd 0f5b5da Ziang Zhang 2026-07-28 Align all figure captions with captions_20260728_final.docx
html 7fb2ab0 Ziang Zhang 2026-07-28 Build site: FigureS6 gallery code shows the verified-install write
html f781754 Ziang Zhang 2026-07-28 Build site: FigureS6 caption on omitted unthresholded markers
Rmd aeb91c8 Ziang Zhang 2026-07-28 Panel titles list only the markers the gate actually applied
html 3d96bb2 Ziang Zhang 2026-07-28 Build site: FigureS6 header note on the curated threshold file
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
html 92021bf Ziang Zhang 2026-07-02 Build site.
Rmd db7a5cd Ziang Zhang 2026-07-02 Recover 3 data-provenance gaps into pipeline scripts
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/FigureS6.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 S6/"
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 S6a/S6b (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/FigureS6.Rmd
# Fig. S6a, 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))
# ============================================================
# s6a/s6b: 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_s6a <- make_mean_loading_heatmap(df_organ, "Mean GP loading by tissue (organ_simplified)")
ggsave(paste0(figure_path, "s6a.pdf"), p_s6a, width = 9, height = 5)

df_level1 <- mean_loading_long(L_cd69_sub, meta_hm$annotation_level1, cd69_top_gps_sorted)
p_s6b <- make_mean_loading_heatmap(df_level1, "Mean GP loading by cell type (level1)")
ggsave(paste0(figure_path, "s6b.pdf"), p_s6b, width = 7, height = 5)

Version Author Date
ac650a0 Ziang Zhang 2026-07-30
d538aa2 Ziang Zhang 2026-07-28

Version Author Date
ac650a0 Ziang Zhang 2026-07-30
d538aa2 Ziang Zhang 2026-07-28

Extended Data Fig. 6a, 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

# ============================================================
# s6c-s6f: 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.
figs6_gating <- c("GP29" = "s6c", "GP58" = "s6d", "GP22" = "s6e", "GP68" = "s6f")
for (gp in names(figs6_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, figs6_gating[gp], ".pdf")
  )
}

Version Author Date
ac650a0 Ziang Zhang 2026-07-30
d538aa2 Ziang Zhang 2026-07-28

Version Author Date
ac650a0 Ziang Zhang 2026-07-30
d538aa2 Ziang Zhang 2026-07-28

Version Author Date
ac650a0 Ziang Zhang 2026-07-30
d538aa2 Ziang Zhang 2026-07-28

Version Author Date
ac650a0 Ziang Zhang 2026-07-30
d538aa2 Ziang Zhang 2026-07-28

Extended Data Fig. 6c-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_1.8.7   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