Figure 2: Individual T cells deploy sparse combinations of gene programs that expand with activation
Ziang Zhang
Last updated: 2026-09-09
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immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 |
| 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 |
| html | 4950a16 | Ziang Zhang | 2026-07-29 | Build site: Fig. 2b/2c at 60 bins |
| html | c69d552 | Ziang Zhang | 2026-07-29 | Build site: Fig. 2b/2c on linear axes |
| Rmd | 070c9b0 | Ziang Zhang | 2026-07-29 | Fig. 2b/2c: back to linear x axes |
| 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 |
| 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 | 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 | 826226c | Ziang Zhang | 2026-07-26 | Fix line range for Fig2F code snippet after decoupling R from subsample |
| html | 826226c | Ziang Zhang | 2026-07-26 | Fix line range for Fig2F code snippet after decoupling R from subsample |
| Rmd | b197499 | Ziang Zhang | 2026-07-16 | Restructure main figures (renumber + split Figure 1) |
| html | b197499 | Ziang Zhang | 2026-07-16 | Restructure main figures (renumber + split Figure 1) |
| html | 6d1c6c0 | Ziang Zhang | 2026-07-04 | Fix TableS1 to use non-thymocyte (not healthy-restricted) AUC, matching published Table S1 |
| html | 8f0198e | Ziang Zhang | 2026-07-03 | Build site. |
| html | 8b50a78 | Ziang Zhang | 2026-07-03 | Build site. |
| html | 92021bf | Ziang Zhang | 2026-07-02 | Build site. |
| 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/Figure2.R.
The code below is shown for reference (not re-executed on this page);
the images are its pre-rendered output. These panels quantify how many
cells and genes are “active” in each gene program, over non-thymocyte
cells.
Setup
Data loading (non-thymocyte cells), shared across all panels below.
library(ggplot2)
library(dplyr)
library(Matrix) # protein matrix is a dgCMatrix; attach for `[` dispatch
data_path <- "data/"
figure_path <- "figures/final-selected/Figure 2/"
source("code/R/volcano_helpers.R") # plot_gp_signature_volcano(), normalize_maxabs()
# ============================================================
# Load data (non-thymocyte cells only, matching the former Figure 1)
# ============================================================
flashier_snmf_summary <- readRDS(paste0(data_path, "flashier_snmf_summary.rds"))
L_pm_filtered <- readRDS(paste0(data_path, "L_pm_filtered.rds"))
F_pm_filtered <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
seurat_meta <- readRDS(paste0(data_path, "igt1_96_withtotalvi20260206_clean_ADTonly.Rds"))@meta.data
seurat_meta_filtered <- seurat_meta[rownames(L_pm_filtered), ]
protein_mat_normalized_lognorm <- readRDS(paste0(data_path, "protein_mat_normalized_lognorm.rds"))
protein_mat_normalized_lognorm <- protein_mat_normalized_lognorm[rownames(L_pm_filtered), "CD44"]
level1_order <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN")
non_thymo_cells <- seurat_meta_filtered$cellID[seurat_meta_filtered$annotation_level1 != "thymocyte"]
L_pm_filtered <- L_pm_filtered[non_thymo_cells, ]
seurat_meta_filtered <- seurat_meta_filtered[non_thymo_cells, ]
protein_mat_normalized_lognorm <- protein_mat_normalized_lognorm[non_thymo_cells](a) GP1 signature volcano
# ============================================================
# 2A: GP1 signature volcano (see code/R/volcano_helpers.R header)
# ============================================================
F_pm_filtered_1d <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
colnames(F_pm_filtered_1d) <- paste0("GP", seq_len(ncol(F_pm_filtered_1d)))
F_pm_normalized_1d <- normalize_maxabs(F_pm_filtered_1d)
mean_shifted_log_expr <- readRDS(paste0(data_path, "mean_shifted_log_expr.rds"))
p_2A <- plot_gp_signature_volcano("GP1", F_pm_normalized_1d, mean_shifted_log_expr, threshold = 0.1, n_label = 47, bg_alpha = 0.2)
ggsave(filename = paste0(figure_path, "2A.pdf"), plot = p_2A, width = 7, height = 5.5)
Fig. 2a. Gene scores for GP1, a baseline program capturing the pan-T cell transcriptional profile. Each point represents a gene. The x-axis shows the GP1 gene score, representing the magnitude and direction of gene regulation within the program and scaled to a maximum absolute value of 1. The y-axis shows mean expression across all T cells (log-normalized).
(b) Active-cell proportion per GP
# ============================================================
# 2B: histogram of the proportion of highly-active cells per GP
# ============================================================
L_pm_norm_col <- L_pm_filtered / matrix(apply(L_pm_filtered, 2, function(x) max(x)), nrow = nrow(L_pm_filtered), ncol = ncol(L_pm_filtered), byrow = TRUE)
gp_active_cell_counts <- colSums((L_pm_norm_col) > 1e-1)
gp_active_cell_prop <- gp_active_cell_counts / nrow(L_pm_norm_col)
p_2B <- ggplot(data.frame(prop = gp_active_cell_prop), aes(x = prop)) +
geom_histogram(bins = 60, fill = "steelblue", color = "white") + # 60, matching 2C
scale_x_continuous(labels = scales::label_percent()) +
labs(x = "Proportion of highly active cells per GP", y = "Count",
title = "Histogram of highly active cells per GP (proportion)") +
theme_minimal(base_size = 13)
ggsave(filename = paste0(figure_path, "2B.pdf"), plot = p_2B, width = 6, height = 4, dpi = 300)
Fig. 2b. Histogram showing the fraction of cells in which each GP was highly active (loading greater than 0.1 in a given cell).
(c) Active-gene count per GP
# ============================================================
# 2C: histogram of the number of highly-active genes per GP
# ============================================================
F_pm_norm_col <- F_pm_filtered / matrix(apply(F_pm_filtered, 2, function(x) max(abs(x))), nrow = nrow(F_pm_filtered), ncol = ncol(F_pm_filtered), byrow = TRUE)
gp_active_gene_counts <- colSums(abs(F_pm_norm_col) > 0.25)
p_2C <- ggplot(data.frame(count = gp_active_gene_counts), aes(x = count)) +
# 60 bins, ~7 genes wide over the 1..421 range.
geom_histogram(bins = 60, fill = "steelblue", color = "white") +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Number of highly active genes per GP", y = "Count",
title = "Histogram of highly active genes per GP") +
theme_minimal(base_size = 13)
ggsave(filename = paste0(figure_path, "2C.pdf"), plot = p_2C, width = 6, height = 4, dpi = 300)
Fig. 2c. Histogram showing the number of highly active genes per GP (genes with an absolute score greater than 0.25 in a given GP).
(d) Active-GP count by lineage
# ============================================================
# 2D: boxplot of active-GP-count per cell, by major lineage
# ============================================================
gp_active_cell_counts_level1 <- dplyr::bind_rows(lapply(level1_order, function(grp) {
cells_grp <- seurat_meta_filtered$cellID[seurat_meta_filtered$annotation_level1 == grp]
L_grp <- L_pm_filtered[seurat_meta_filtered$cellID %in% cells_grp, , drop = FALSE]
data.frame(Group = grp, Active_Cell_Counts = rowSums(L_grp > 1e-1))
}))
group_counts <- gp_active_cell_counts_level1 %>%
group_by(Group) %>% summarise(n = n(), .groups = "drop") %>%
mutate(Group_Label = paste0(Group, "\n(n=", n, ")")) %>%
mutate(Group = factor(Group, levels = level1_order)) %>%
arrange(Group) %>% mutate(Group_Label = factor(Group_Label, levels = Group_Label))
plot_df_g <- gp_active_cell_counts_level1 %>% left_join(group_counts, by = "Group")
p_2D <- ggplot(plot_df_g, aes(x = Group_Label, y = Active_Cell_Counts, fill = Group)) +
geom_boxplot(outlier.size = 0.4, width = 0.6, alpha = 0.8, color = "gray40") +
scale_fill_manual(values = ZemmourLib::immgent_colors$level1) +
labs(title = "Active Gene Programs per Group", x = "Cell Group (Annotation Level 1)", y = "Number of highly active GPs") +
theme_minimal(base_size = 13) +
theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5), axis.text.x = element_text(size = 11, angle = 45, hjust = 1), axis.text.y = element_text(size = 12), axis.title.y = element_text(size = 13, face = "bold"), legend.position = "none", panel.grid.minor = element_blank())
ggsave(filename = paste0(figure_path, "2D.pdf"), plot = p_2D, width = 6, height = 4, dpi = 300)
Fig. 2d. Box plot showing the number of highly active GPs per cell across T cell lineages.
(e) Active-GP count, activated vs. resting
# ============================================================
# 2E: boxplot of active-GP-count per cell, activated vs resting
# ============================================================
cells_activated <- seurat_meta_filtered$cellID[seurat_meta_filtered$annotation_level2_group == "activated"]
gp_active_cell_counts_activated <- rowSums(L_pm_filtered[seurat_meta_filtered$cellID %in% cells_activated, ] > 1e-1)
cells_resting <- seurat_meta_filtered$cellID[seurat_meta_filtered$annotation_level2_group == "resting"]
gp_active_cell_counts_resting <- rowSums(L_pm_filtered[seurat_meta_filtered$cellID %in% cells_resting, ] > 1e-1)
gp_active_cell_counts_df <- data.frame(
Group = c(rep("Activated", length(gp_active_cell_counts_activated)), rep("Resting", length(gp_active_cell_counts_resting))),
Active_Cell_Counts = c(gp_active_cell_counts_activated, gp_active_cell_counts_resting))
p_2E <- ggplot(gp_active_cell_counts_df, aes(x = Group, y = Active_Cell_Counts, fill = Group)) +
geom_boxplot(outlier.size = 0.4, width = 0.6, alpha = 0.8, color = "gray40") +
scale_fill_manual(values = c("Activated" = "#1f78b4", "Resting" = "#e31a1c")) +
labs(title = "", x = "", y = "Number of highly active GPs") +
theme_minimal(base_size = 13) +
theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5), axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12), axis.title.y = element_text(size = 13, face = "bold"), legend.position = "none")
ggsave(filename = paste0(figure_path, "2E.pdf"), plot = p_2E, width = 6, height = 4, dpi = 300)
Fig. 2e. Box plot showing the number of highly active GPs per cell in activated and resting T cells.
(f) CD44 vs. active-GP count
# ============================================================
# 2F: scatter of CD44 protein level vs. number of active GPs per cell
# ============================================================
gp_active_cell_counts_all <- rowSums(L_pm_filtered > 1e-1)
gp_cd44_df <- data.frame(CD44_Protein_Level = protein_mat_normalized_lognorm, Active_GP_Counts = gp_active_cell_counts_all)
gp_cd44_df_nz <- gp_cd44_df %>% dplyr::filter(CD44_Protein_Level > 0)
R <- cor(gp_cd44_df_nz$CD44_Protein_Level, gp_cd44_df_nz$Active_GP_Counts, use = "complete.obs") # on all non-zero-CD44 cells, not just the plotted subsample
set.seed(123)
df_nz <- gp_cd44_df_nz %>% sample_n(min(10000, nrow(.))) # subsample only for scatter rendering
p_2F <- ggplot(df_nz, aes(CD44_Protein_Level, Active_GP_Counts)) +
geom_point(alpha = 0.1, size = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(title = "CD44 Protein Level vs Number of Active GPs", x = "CD44 Protein Level (log-normalized)", y = "Number of Active GPs") +
annotate("text", x = min(df_nz$CD44_Protein_Level, na.rm = TRUE) + 0.5, y = max(df_nz$Active_GP_Counts, na.rm = TRUE) - 1, label = paste0("R = ", round(R, 2)), size = 4) +
theme_minimal(base_size = 13)
ggsave(filename = paste0(figure_path, "2F.pdf"), plot = p_2F, width = 6, height = 4, dpi = 300)
Fig. 2f. Scatterplot showing the number of highly active GPs per cell correlated with CD44 protein expression (CITE-seq; log-normalized counts). The blue line indicates the least-squares fit; Pearson’s r = 0.44.
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