Figure S6: GP loadings recover protein-gated populations across GPs
Ziang Zhang
Last updated: 2026-07-27
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Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 |
Both pages are produced by script/FigureS6.R,
which shares its CITE-seq setup and gating logic with Figure 6c-f 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 roughly
a minute and a half to render ~25 GPs x 2 embeddings each); the images
are its pre-rendered output.
Setup
# Figure S6. GP loadings recover protein-gated populations across GPs.
#
# Extension of Figure 6c-f to all well-aligned GPs (see
# figures/Previous/bits/Figure S6/FigureS6_caption.md), shown as two
# gallery pages (s6-1 and s6-2). For each GP, cells are shown twice on the
# same MDE embedding: left, cells passing the GP's curated protein gate;
# right, an equally sized set of cells with the highest GP loading.
#
# Source: ported from gated_protein_loading_plot.R's live gallery
# section (the ~450 preceding lines of commented-out single-GP exploratory
# calls are dropped -- they never produced a saved output).
#
# Required inputs (data/), read via code/R/citeseq_shared_setup.R below --
# see code/README.md's "Data provenance" table for the full picture:
# L_pm_filtered.rds, F_pm_filtered.rds [code/pipeline/01b_filter_cells.R]
# igt1_96_..._ADTonly.Rds [primary input Seurat object]
# protein_mat_normalized_lognorm.rds [code/other/prepare_citeseq_protein_matrices_20260206.R]
# umap_result.rds [gap, no producer script here]
# protein_flash_selected_summary_lognorm_backfit200.rds
# [code/other/fit_citeseq_fixed_loading_ebmf_20260206.R]
# TableS4_citeseq_qc_20250513.csv [external: manuscript's own Table S4]
# Thresholds_Selected_Proteins.csv [code/pipeline/03_protein_thresholds.R]
# CITEseq_markers_full.rds [code/pipeline/04_protein_projection.R, using the
# non-backfit200 protein summary -- see caveat above]
library(ggplot2)
library(dplyr)
library(patchwork)
library(Matrix)
data_path <- "data/"
figure_path <- "figures/final-selected/Figure S6/"
source("code/R/gated_protein_helpers.R")
source("code/R/citeseq_shared_setup.R")Gallery pages
# GPs shown in main Figure 6 (panels 6c-6f) are excluded from this gallery.
GPs_fig6 <- c("GP171", "GP23", "GP12", "GP80")
other_GPs <- setdiff(well_aligned_gps, GPs_fig6)
# A few GPs get slightly larger highlighted points for visibility even at
# high cell counts.
enlarge_gps <- c("GP8", "GP30", "GP170", "GP107")
plots_all <- lapply(other_GPs, function(gp) {
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
)
})
v_bar <- ggplot() + theme_void() + theme(plot.background = element_rect(fill = "grey60", color = NA))
# Sorted gallery: GPs in numerical order, one PDF per page (2 pages here),
# 2 GP-units per row x 6 rows = 12 GPs per page; panels labeled a, b, c...
sort_idx <- order(as.integer(sub("^GP", "", other_GPs)))
plots_sorted <- plots_all[sort_idx]
n_pages_sorted <- ceiling(length(plots_sorted) / 12)
for (page_idx in seq_len(n_pages_sorted)) {
idx_start <- (page_idx - 1) * 12 + 1
idx_end <- min(page_idx * 12, length(plots_sorted))
page_plots <- plots_sorted[idx_start:idx_end]
n_on_page <- length(page_plots)
labeled_page_plots <- lapply(seq_len(n_on_page), function(i) {
unit <- page_plots[[i]]
p1_labeled <- unit[[1]] + labs(tag = letters[i]) + theme(plot.tag = element_text(size = 14, face = "bold"))
p1_labeled + unit[[2]] + plot_layout(ncol = 2)
})
rows_list <- lapply(1:6, function(r) {
i_left <- (r - 1) * 2 + 1
i_right <- (r - 1) * 2 + 2
gp_left <- if (i_left <= n_on_page) labeled_page_plots[[i_left]] else plot_spacer()
gp_right <- if (i_right <= n_on_page) labeled_page_plots[[i_right]] else plot_spacer()
(gp_left | v_bar | gp_right) + plot_layout(widths = c(1, 0.03, 1))
})
combined <- wrap_plots(rows_list, ncol = 1)
# cairo_pdf() does not reliably truncate/overwrite an existing file of a
# different size in place -- remove any stale output first.
out_page <- paste0(figure_path, sprintf("s6-%d.pdf", page_idx))
if (file.exists(out_page)) unlink(out_page)
graphics.off()
cairo_pdf(out_page, width = 13, height = 18)
showtext::showtext_begin()
print(combined)
showtext::showtext_end()
dev.off()
}
Fig. S6. Extension of Figure 6c-f to all well-aligned GPs, shown as two gallery pages (s6-1 and s6-2). For each GP, cells are shown twice on the same MDE embedding: left, cells passing a protein gate built from the GP’s curated marker signature (positive markers above, negative below), with the gate size reported as “Matched n”; right, an equally sized set of cells with the highest GP loading. Highlighted cells are colored by two-dimensional density and all other cells are grey; thymocytes, proliferating, and “miniverse” cells are excluded. Each panel is labeled with its GP and protein signature.
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