Figure 6: Surface proteins associated with GP activity
Ziang Zhang
Last updated: 2026-09-02
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immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 | 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 | 58eab27 | Ziang Zhang | 2026-07-28 | Build site: Fig. 6i-k caption on the curated CD69 GP subset |
| Rmd | 589a0ed | Ziang Zhang | 2026-07-28 | Fig. 6i-k: say the 10 CD69 GPs are curated, not the top 10 |
| 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 |
| 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 |
| Rmd | 06552b8 | Ziang Zhang | 2026-07-14 | Update Figure 6b heatmap layout |
| html | 06552b8 | Ziang Zhang | 2026-07-14 | Update Figure 6b heatmap layout |
| 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 except (a) are produced by script/Figure6.R,
which shares its CITE-seq setup with Extended
Data Figure 6 and Extended Data Figure 7
via code/R/citeseq_shared_setup.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(ggrepel)
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 6/"
source("code/R/gated_protein_helpers.R")
# 6a: hand-drawn schematic -- not code-generated, no output here.Panels (b) onward additionally load the shared CITE-seq cell/protein setup:
# ============================================================
# Load data (shared with FigureS6.R and FigureS7.R)
# ============================================================
source("code/R/citeseq_shared_setup.R")(a) Protein-signature schematic
This panel is a hand-drawn schematic, not generated from code.
Fig. 6a. Schematic illustrating how protein signatures were defined for each GP. Holding the cell-loading matrix L fixed from the GP model, a protein factor matrix was estimated by EBMF from the paired CITE-seq protein measurements, Y ≈ LUᵀ (cells × proteins matrix), yielding a protein signature U (proteins × GPs matrix) for each GP. See Methods: fitting CITE-seq protein programs with fixed scRNA loadings.
(b, c) KLRG1 modulation across lineages
# ============================================================
# 6b/6c: KLRG1 modulation (CD8 vs CD4, CD8 vs Treg)
# ============================================================
FlashierDGE_corrected <- function(F1, L1, group1, group2, title_plot = "") {
loadings_group1 <- colMeans(L1[group1, ])
loadings_group2 <- colMeans(L1[group2, ])
mean_change_loadings <- loadings_group1 - loadings_group2
vplot <- data.frame(SYMBOL = names(mean_change_loadings), mean_change_loadings = mean_change_loadings, AveExpr = colMeans(L1[c(group1, group2), ]))
list(diff_factors = vplot)
}
get_klrg1_split <- function(cell_type_label, meta, protein_data, threshold) {
cells <- meta$cellID[meta$annotation_level1 == cell_type_label]
cells <- intersect(cells, rownames(protein_data))
list(pos = cells[protein_data[cells, "KLRG1"] >= threshold], neg = cells[protein_data[cells, "KLRG1"] < threshold])
}
run_checked_dge <- function(group_list, F_mat, L_mat, label) {
if (length(group_list$pos) < 3 || length(group_list$neg) < 3) stop(paste("Insufficient data:", label))
df <- FlashierDGE_corrected(F1 = F_mat, L1 = L_mat, group1 = group_list$pos, group2 = group_list$neg)$diff_factors
if (!"SYMBOL" %in% colnames(df)) df$SYMBOL <- rownames(df)
df
}
plot_target_gps <- function(df, x_var, y_var, label_var, target_gps, highlight_color = "darkorange", background_color = "black",
x_limits = c(-0.5, 0.5), y_limits = c(-0.5, 0.5), background_alpha = 0.5,
xlab = "Difference in Mean Loading", ylab = "Difference in Mean Loading", title = "Comparison of Specific GP Loadings") {
highlight_df <- df %>% filter({{ label_var }} %in% target_gps) %>% mutate(.label_display = as.character({{ label_var }}))
ggplot(df, aes(x = {{ x_var }}, y = {{ y_var }})) +
geom_point(color = background_color, alpha = background_alpha) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "red") +
geom_hline(yintercept = 0, linetype = "dashed", color = "blue") +
geom_vline(xintercept = 0, linetype = "dashed", color = "blue") +
geom_point(data = highlight_df, aes(color = {{ label_var }}), size = 2) +
ggrepel::geom_text_repel(seed = 42, data = highlight_df, aes(label = .label_display, color = {{ label_var }}), max.overlaps = Inf, size = 3.5, box.padding = 0.35, point.padding = 0.5, segment.color = "grey50", show.legend = FALSE) +
scale_color_manual(values = highlight_color, guide = "none") +
coord_cartesian(xlim = x_limits, ylim = y_limits) +
labs(x = xlab, y = ylab, title = title) +
theme_minimal()
}
klrg1_threshold <- threshold_results_subset_manual$Threshold[threshold_results_subset_manual$Protein == "KLRG1"]
cd8_split <- get_klrg1_split("CD8", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)
CD4_split <- get_klrg1_split("CD4", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)
treg_split <- get_klrg1_split("Treg", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)
diff_CD8 <- run_checked_dge(cd8_split, F_pm_filtered, L_pm_filtered, "CD8") %>% rename(mean_change_CD8 = mean_change_loadings, AveExpr_CD8 = AveExpr)
diff_CD4 <- run_checked_dge(CD4_split, F_pm_filtered, L_pm_filtered, "CD4") %>% rename(mean_change_CD4 = mean_change_loadings, AveExpr_CD4 = AveExpr)
diff_Treg <- run_checked_dge(treg_split, F_pm_filtered, L_pm_filtered, "Treg") %>% rename(mean_change_Treg = mean_change_loadings, AveExpr_Treg = AveExpr)
# 6b: CD8 vs CD4
merged_cd4 <- inner_join(diff_CD4, diff_CD8, by = "SYMBOL")
p_6b <- plot_target_gps(
df = merged_cd4, x_var = mean_change_CD8, y_var = mean_change_CD4, label_var = SYMBOL,
target_gps = c("GP10", "GP58", "GP25", "GP26", "GP43"), background_alpha = 0.8, x_limits = c(-0.2, 0.4), y_limits = c(-0.2, 0.4),
highlight_color = c("GP10" = "darkorange2", "GP25" = "blue", "GP43" = "blue", "GP26" = "blue", "GP58" = "darkorange2"),
title = "KLRG1 Modulation: CD8 vs CD4", xlab = "Effect Size in CD8 (KLRG1+ - KLRG1-)", ylab = "Effect Size in CD4 (KLRG1+ - KLRG1-)"
) + theme_bw()
ggsave(paste0(figure_path, "6b.pdf"), p_6b, width = 7, height = 6)
# 6c: CD8 vs Treg
merged_treg <- inner_join(diff_Treg, diff_CD8, by = "SYMBOL")
p_6c <- plot_target_gps(
df = merged_treg, x_var = mean_change_CD8, y_var = mean_change_Treg, label_var = SYMBOL,
target_gps = c("GP6", "GP10", "GP12", "GP27", "GP68", "GP58"), background_alpha = 0.8, x_limits = c(-0.2, 0.4), y_limits = c(-0.2, 0.4),
highlight_color = c("GP10" = "darkorange2", "GP27" = "deeppink", "GP6" = "deeppink", "GP68" = "deeppink", "GP12" = "deeppink", "GP58" = "darkorange2"),
title = "KLRG1 Modulation: CD8 vs Treg", xlab = "Effect Size in CD8 (KLRG1+ - KLRG1-)", ylab = "Effect Size in Treg (KLRG1+ - KLRG1-)"
) + theme_bw()
ggsave(paste0(figure_path, "6c.pdf"), p_6c, width = 7, height = 6)
Fig. 6b, c. GPs associated with KLRG1 protein expression in CD4, Tregs and CD8. Within each lineage, cells are split KLRG1+ versus KLRG1- based on the threshold on the KLRG1 protein, and for every GP the effect size is the difference in its mean activity between KLRG1+ and KLRG1- cells. These effect-size versus effect-size plots compare CD8+ T cells (x-axis) with CD4+ T cells (b) or Treg cells (c) (y-axis); each point is a GP, the dashed red line marks equal effect (y = x), and the dashed blue lines mark zero. Selected GPs are labeled – orange for large, concordant effects in both lineages, and blue/pink for lineage-biased GPs.
(d) Genes up- and down-regulated across the CD69-associated GPs
The ten GPs, and the correlation-based order they are drawn in, are
defined once in code/R/citeseq_shared_setup.R, because Extended Data Fig. 7a, b show the same
GPs in the same order from a different script:
# The CD69-associated GP subset, shared by Figure 6d (the up/down gene heatmap)
# and Figure S7a/S7b (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/FigureS7.Rmd
# Fig. S7a, 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))# ============================================================
# 6d: up/down genes across the 10 curated CD69-associated GPs
# (cd69_top_gps_subset / cd69_corr / cd69_top_gps_sorted come from
# citeseq_shared_setup.R, which Figure S7's s7a/s7b panels share)
# ============================================================
D_scale6 <- diag(1 / apply(F_pm_filtered, 2, function(x) max(abs(x), na.rm = TRUE)))
F_pm_filtered_scaled <- F_pm_filtered %*% D_scale6
colnames(F_pm_filtered_scaled) <- paste0("GP", 1:ncol(F_pm_filtered_scaled))
plot_factor_heatmap <- function(F_matrix, gp_vector, n_top = 5, min_abs_loading = 0.5, transpose = FALSE,
title = "Factor loadings – top genes per GP", low_color = "steelblue", mid_color = "white", high_color = "firebrick", font_size = 9) {
F_sub <- F_matrix[, gp_vector, drop = FALSE]
selected_genes <- lapply(gp_vector, function(gp) {
vals <- F_sub[, gp]
top_pos <- names(sort(vals, decreasing = TRUE))[seq_len(min(n_top, sum(vals > 0)))]
top_neg <- names(sort(vals, decreasing = FALSE))[seq_len(min(n_top, sum(vals < 0)))]
c(top_pos, top_neg)
})
selected_genes <- unique(unlist(selected_genes))
if (min_abs_loading > 0) {
max_abs <- apply(F_sub[selected_genes, , drop = FALSE], 1, function(x) max(abs(x), na.rm = TRUE))
selected_genes <- names(max_abs[max_abs >= min_abs_loading])
}
hc_genes <- hclust(dist(F_sub[selected_genes, , drop = FALSE]))
gene_order <- rownames(F_sub[selected_genes, , drop = FALSE])[hc_genes$order]
plot_df <- F_sub[selected_genes, , drop = FALSE] %>%
as.data.frame() %>%
tibble::rownames_to_column("Gene") %>%
tidyr::pivot_longer(cols = -Gene, names_to = "GP", values_to = "Loading") %>%
mutate(GP = factor(GP, levels = gp_vector), Gene = factor(Gene, levels = gene_order))
limit <- max(abs(plot_df$Loading), na.rm = TRUE)
x_aes <- if (transpose) "Gene" else "GP"
y_aes <- if (transpose) "GP" else "Gene"
ggplot(plot_df, aes(x = .data[[x_aes]], y = .data[[y_aes]], fill = Loading)) +
geom_tile() +
scale_fill_gradient2(low = low_color, mid = mid_color, high = high_color, midpoint = 0, limits = c(-limit, limit), name = "Loading") +
# Axis titles are the faceting variables themselves ("Gene" / "GP").
labs(title = title, x = x_aes, y = y_aes) +
theme_minimal(base_size = font_size) +
theme(axis.text.x = element_text(angle = if (transpose) 90 else 45, hjust = 1, size = font_size), axis.text.y = element_text(size = font_size), panel.grid = element_blank(), plot.title = element_text(face = "bold"))
}
p_heatmap <- plot_factor_heatmap(F_matrix = F_pm_filtered_scaled, gp_vector = cd69_top_gps_sorted, n_top = 5, font_size = 9, transpose = TRUE, low_color = "#4DAF4A", mid_color = "white", high_color = "#984EA3")
corr_strip_df <- data.frame(GP = factor(cd69_top_gps_sorted, levels = cd69_top_gps_sorted), Correlation = cd69_corr[cd69_top_gps_sorted], x = "Corr")
corr_limit <- max(abs(corr_strip_df$Correlation))
p_corr_strip <- ggplot(corr_strip_df, aes(x = x, y = GP, fill = Correlation)) +
geom_tile() +
scale_fill_gradient2(low = "royalblue", mid = "white", high = "tomato", midpoint = 0, limits = c(-corr_limit, corr_limit), name = "Corr\n(CD69)") +
labs(x = NULL, y = NULL) +
theme_minimal(base_size = 9) +
theme(axis.text.x = element_text(size = 9, angle = 45, hjust = 1), axis.text.y = element_blank(), axis.ticks.y = element_blank(), panel.grid = element_blank())
p_6d <- p_corr_strip + p_heatmap + patchwork::plot_layout(widths = c(0.06, 1), guides = "collect")
ggsave(paste0(figure_path, "6d.pdf"), p_6d, width = 11, height = 5)
Fig. 6d. GPs associated with CD69 protein expression. Heatmap of the genes most strongly up- and down-regulated across ten representative CD69-associated GPs, ordered by the Spearman correlation between GP activity and CD69 expression, with the left strip indicating that correlation (blue-white-red). The genes shown are, per GP, its top five positively and top five negatively scoring genes, then restricted to those reaching |score| >= 0.5 in at least one of the ten (green-white-purple). Mean activity of the same ten GPs across tissues and lineages is shown in Extended Data Fig. 7a, b.
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: eight are positively correlated (ranks 1, 3, 4,
5, 6, 10, 12 and 14 of 200) and GP58/GP171 are the two most
negatively correlated GPs of all 200.
script/verify_cd69_gp_ranking.R recomputes the correlation
over all 200 GPs and fails if this wording and the code drift apart.
(e-j) Protein-gated vs. GP-loading populations
# ============================================================
# 6e-6j: protein-gate vs. GP-loading comparison for the 6 curated main-figure
# GPs (df_markers2, thymocyte/proliferating/miniverse_cells, L_pm_for_gating,
# select_proteins, threshold_results_subset_manual, enlarge_gps all come from
# citeseq_shared_setup.R above)
# ============================================================
# Panel lettering is carried by this named vector and the loop iterates over its
# names, so a GP can never be drawn under another GP's letter. (An earlier
# version kept the GP list and the letters in two separate vectors and assigned
# the letters positionally, which silently permuted three of the panels.)
fig6_gating <- c("GP171" = "6e", "GP12" = "6f", "GP80" = "6g", "GP23" = "6h", "GP77" = "6i", "GP8" = "6j")
for (gp in names(fig6_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, fig6_gating[gp], ".pdf")
)
}
Fig. 6e-j. Examples of gating strategies used to identify GP-active cells, for (e) GP171, (f) GP12, (g) GP80, (h) GP23, (i) GP77 and (j) GP8. For each GP, cells are shown twice on the same MDE embedding. Left, cells selected by the proposed protein gate, built from the GP’s curated marker signature (positive markers above, and negative markers below), with the gate size reported in the panel title as “Matched n”; 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. Four further GPs are gated the same way in Extended Data Fig. 7c-f.
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