Last updated: 2026-09-10

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Knit directory: immgenT-GP-analysis/analysis/

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File Version Author Date Message
Rmd dca7ffb Ziang Zhang 2026-09-10 Strip the code-narrating prose from every page; fold code chunks by default
html 54718c4 Ziang Zhang 2026-09-10 Build site: the four mean-loading heatmaps without their titles
html 936ed6e Ziang Zhang 2026-09-10 Build site: Extended Data 2d without the DP lineage
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 bf4f612 Ziang Zhang 2026-09-09 Build site: Extended Data back to 1-8
Rmd 6f01135 Ziang Zhang 2026-09-09 Pull the tissue figure back out of Extended Data; ED is 1-8 again
html 6f01135 Ziang Zhang 2026-09-09 Pull the tissue figure back out of Extended Data; ED is 1-8 again
html 8c0c07f Ziang Zhang 2026-09-09 Build site: Extended Data Figure 5 on the 32-GP union
Rmd 9fab2f6 Ziang Zhang 2026-09-09 Extended Data Figure 5: show the union of both tissue-associated GP sets
html b0c1d19 Ziang Zhang 2026-09-09 Build site: Extended Data Figure 5b recoloured
Rmd 5be33df Ziang Zhang 2026-09-09 Extended Data Figure 5b: purple is the positive end, green the negative
html a7a481f Ziang Zhang 2026-09-09 Build site: the rebuilt Figure 1d and the Extended Data renumbering
Rmd c233cd8 Ziang Zhang 2026-09-09 Extended Data reorganisation: split the tissue/cluster figure, renumber 3-8
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 5d3b86c Ziang Zhang 2026-09-03 Build site: Extended Data Figure 5 page rebuilt after the recolouring
Rmd 2101847 Ziang Zhang 2026-09-03 Extended Data Figure 5: colour each lineage row on its own
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
html ae03072 Ziang Zhang 2026-08-19 Build site: six pages rebuilt after the prose cleanup
Rmd adc2327 Ziang Zhang 2026-08-19 Site prose: finish taking internal notes off the pages
html eeca07b Ziang Zhang 2026-08-05 Keep pre-refactor provenance in panel comments off the published pages
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 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 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 ffe285c Ziang Zhang 2026-07-27 Reorganize figures/ and untrack local-only exploration notes
html f7d90e7 Ziang Zhang 2026-07-26 Build site.
Rmd b0b2b2f Ziang Zhang 2026-07-26 Tidy Figure S5 page: name the S5a/S5b/colorbar panels
html fe93d0d Ziang Zhang 2026-07-23 Build site.
Rmd 61de7cb Ziang Zhang 2026-07-23 Reflect single-matching pipeline on the Figure S5 page
html 9862b6d Ziang Zhang 2026-07-23 Build site.
Rmd 98d2924 Ziang Zhang 2026-07-23 Reword Figure S5 page for a publication audience
html 7ddbdb4 Ziang Zhang 2026-07-23 Build site.
Rmd b138063 Ziang Zhang 2026-07-23 Reformat Figure S5 page: lead with the figure, concise methods, link
html 9398c72 Ziang Zhang 2026-07-23 Publish Figure S5 workflowr page
Rmd b9f4f58 Ziang Zhang 2026-07-23 Add Figure S5: EBMF vs matched-RQVI level2-cluster comparison

All panels are produced by script/FigureS5.R. The code below is shown for reference; 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 S5/"
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 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))
# ============================================================
# s5a/s5b: 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 7d 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_s5a <- make_mean_loading_heatmap(df_organ, "Mean GP loading by tissue (organ_simplified)")
ggsave(paste0(figure_path, "s5a.pdf"), p_s5a, width = 9, height = 5)

df_level1 <- mean_loading_long(L_cd69_sub, meta_hm$annotation_level1, cd69_top_gps_sorted)
p_s5b <- make_mean_loading_heatmap(df_level1, "Mean GP loading by cell type (level1)")
ggsave(paste0(figure_path, "s5b.pdf"), p_s5b, width = 7, height = 5)

Version Author Date
c21f51f Ziang Zhang 2026-09-10
6f01135 Ziang Zhang 2026-09-09
9fab2f6 Ziang Zhang 2026-09-09
c233cd8 Ziang Zhang 2026-09-09

Version Author Date
c21f51f Ziang Zhang 2026-09-10
6f01135 Ziang Zhang 2026-09-09
9fab2f6 Ziang Zhang 2026-09-09
5be33df Ziang Zhang 2026-09-09
c233cd8 Ziang Zhang 2026-09-09

Extended Data Fig. 5a, 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

# ============================================================
# s5c-s5f: protein-gate vs. GP-loading comparison for the 4 curated
# supplementary GPs. Same helper, same inputs and same panel geometry as
# Figure 7e-7j -- only the GPs differ, and the two sets are disjoint.
# ============================================================
# As in Figure7.R, the loop iterates over the names of the letter map so a GP
# cannot be drawn under another GP's letter.
figs5_gating <- c("GP29" = "s5c", "GP58" = "s5d", "GP22" = "s5e", "GP68" = "s5f")
for (gp in names(figs5_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, figs5_gating[gp], ".pdf")
  )
}

Version Author Date
c21f51f Ziang Zhang 2026-09-10

Version Author Date
c21f51f Ziang Zhang 2026-09-10

Version Author Date
c21f51f Ziang Zhang 2026-09-10

Version Author Date
c21f51f Ziang Zhang 2026-09-10

Extended Data Fig. 5c-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