Extended Data Figure 6: Surface proteins association with GP
Ziang Zhang
Last updated: 2026-09-10
Checks: 7 0
Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 | 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 | 796eeba | Ziang Zhang | 2026-09-04 | Build site: the three corrected captions |
| Rmd | 2344a4a | Ziang Zhang | 2026-09-04 | Three captions corrected where the published text and the panel disagree |
| 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 |
| Rmd | 1e5721a | Ziang Zhang | 2026-09-02 | Extended Data 5-7 renumbered, and Figure S5 assembled as one stacked figure |
| 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 | 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 7 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")
# 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. 7d – 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 7d (the up/down gene heatmap)
# and Figure S6a/S6b (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))# ============================================================
# 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 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_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)
Extended Data Fig. 6a, 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
# ============================================================
# s6c-s6f: 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.
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")
)
}
Extended Data Fig. 6c-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