Extended Data Figure 6: CD69-associated GPs and further surface-protein gating strategies
Ziang Zhang
Last updated: 2026-07-30
Checks: 7 0
Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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.
This figure was reworked on 2026-07-28. It used to
be a two-page gallery (s6-1, s6-2) of all 23
well-aligned GPs that Figure 6 did not show. The gallery is retired;
four of its GPs are kept here as panels (c)-(f), two were promoted into
the main figure (Fig. 6i, j), and the
remaining 17 are no longer published. Panels (a) and (b) moved here from
Figure 6, where they were the published 6j and 6k.
script/FigureS6.R carries the full old-to-new table in its
header.
Re-lettered again on 2026-07-30. The protein-program heatmap that was this figure’s first panel (the published 6b) became a standalone figure, Extended Data Figure 5, so every panel after it dropped one letter: the b-g of 2026-07-28 are now a-f.
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)
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")
)
}
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 (Extended Data Table 7), 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