Figure 1: Overview
Ziang Zhang
Last updated: 2026-07-15
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immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c7dafe5 | Ziang Zhang | 2026-07-15 | Switch Figure 1C to GP-highlight gene-program network |
| html | c7dafe5 | Ziang Zhang | 2026-07-15 | Switch Figure 1C to GP-highlight gene-program network |
| Rmd | cac4f30 | Ziang Zhang | 2026-07-14 | Replace Figure 1C heatmap with gene-program network |
| html | cac4f30 | Ziang Zhang | 2026-07-14 | Replace Figure 1C heatmap with gene-program network |
| Rmd | c2b3360 | Ziang Zhang | 2026-07-13 | Use log-scale axes for Fig 1E/1F and add Fig S1E gene-vs-cell sparsity scatter |
| html | c2b3360 | Ziang Zhang | 2026-07-13 | Use log-scale axes for Fig 1E/1F and add Fig S1E gene-vs-cell sparsity scatter |
| html | 92021bf | Ziang Zhang | 2026-07-03 | 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 except (B) are produced by script/Figure1.R.
The code below is shown for reference (not re-executed on this page,
since some steps such as the panel-C heatmap are slow); the images are
its pre-rendered output.
Setup
Data loading, shared across all panels below.
library(ggplot2)
library(dplyr)
library(scattermore)
library(ComplexHeatmap)
library(circlize)
library(tibble)
library(Matrix) # protein_mat_normalized_lognorm is a dgCMatrix; must be
# attached (not just loaded) for `[` subsetting to dispatch
data_path <- "data/"
figure_path <- "figures/generated/Figure 1/"
source("code/R/volcano_helpers.R") # plot_gp_signature_volcano() for panel 1D
# ============================================================
# Load data
# ============================================================
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"]
mde_result <- readRDS(paste0(data_path, "umap_result.rds"))
colnames(mde_result) <- c("MDE_1", "MDE_2")
mde_result <- mde_result[rownames(L_pm_filtered), ]
df_mde <- as.data.frame(mde_result)Panels E-I additionally restrict to non-thymocyte cells:
# Drop thymocytes from all downstream cell-level visualizations (matches
# Figure_Overview.R).
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) Global MDE by lineage
# ============================================================
# 1A: Global MDE colored by major lineage, subsampled per lineage
# ============================================================
set.seed(1)
df_mde_a <- df_mde %>% tibble::rownames_to_column("cellID")
plot_df <- df_mde_a %>%
inner_join(seurat_meta_filtered %>% select(cellID, annotation_level1), by = "cellID") %>%
filter(annotation_level1 != "thymocyte")
max_total <- 1000000
min_per_group <- 300
cap_per_group <- 20000
group_sizes <- plot_df %>% count(annotation_level1, name = "n")
G <- nrow(group_sizes)
base_per_group <- ceiling(max_total / max(G, 1))
sample_plan <- group_sizes %>% mutate(n_take = pmin(n, pmax(min_per_group, pmin(cap_per_group, base_per_group))))
plot_df_sub <- plot_df %>%
group_by(annotation_level1) %>%
group_modify(~ dplyr::slice_sample(.x, n = sample_plan$n_take[sample_plan$annotation_level1 == .y$annotation_level1])) %>%
ungroup()
p_1A <- ggplot(plot_df_sub, aes(x = MDE_1, y = MDE_2)) +
scattermore::geom_scattermore(aes(color = annotation_level1), pointsize = 1.2) +
scale_color_manual(values = ZemmourLib::immgent_colors$level1) +
coord_equal() +
theme_classic() +
labs(title = "MDE: Annotation Level 1", x = "MDE 1", y = "MDE 2", color = "Cell Type") +
theme(legend.text = element_text(size = 10), legend.key.size = unit(1.5, "lines")) +
guides(color = guide_legend(override.aes = list(size = 4)))
ggsave(filename = paste0(figure_path, "1A.pdf"), plot = p_1A, width = 5, height = 5)
Fig. 1A. Global MDE embedding of all non-thymocyte cells, colored by major lineage (cells subsampled per lineage for visualization).
(B) Study design schematic
This panel is a hand-drawn schematic, not generated from R – there is
no code or pre-rendered image to show here. See
figures/final-selected/bits/Figure 1/1B.pdf for the
published panel.
(C) Gene-program network
# ============================================================
# 1C: gene-program network -- the 200 GPs linked by their shared top signature
# genes, with a selected set of GPs highlighted. Each highlighted GP's color runs
# along its edges to its top signature genes (which are labeled); non-highlighted
# GPs are grey. Formal version: no legend / no GP-index labels -- the color->GP
# mapping and interpretation are in the Fig 1C caption (analysis/Figure1.Rmd).
# ============================================================
suppressPackageStartupMessages({
library(igraph); library(tidygraph); library(ggraph)
})
N_TOP <- 5 # top up-genes taken per GP
SIG_THR <- 0.1 # min per-GP-normalized loading
GP_HIGHLIGHTS <- c( # GP -> highlight color
GP68 = "pink2", GP58 = "orange2", GP35 = "purple", GP171 = "blue",
GP1 = "cyan2", GP56 = "red2", GP161 = "brown", GP6 = "green2",
GP7 = "green3", GP196 = "yellow3")
GP_COL <- "darkgrey" # non-highlighted GP nodes
GENE_COL <- "#C7A76C" # gene nodes (tan)
Fn <- F_pm_filtered
colnames(Fn) <- paste0("GP", seq_len(ncol(Fn)))
Fn <- sweep(Fn, 2, apply(abs(Fn), 2, max), "/") # per-GP (column) max-abs norm
GPs <- colnames(Fn)
# each GP -> its top up-regulated signature genes (normalized loading >= SIG_THR)
top_up <- lapply(GPs, function(j) { x <- Fn[, j]
u <- names(sort(x, decreasing = TRUE))[1:N_TOP]; u[x[u] >= SIG_THR] })
names(top_up) <- GPs
edges <- do.call(rbind, lapply(GPs, function(g)
if (length(top_up[[g]])) data.frame(GP = g, Gene = top_up[[g]]) else NULL))
# bipartite GP<->gene graph (every GP + all its top genes); plain FR layout
gi <- graph_from_data_frame(edges, directed = FALSE)
g <- as_tbl_graph(gi, directed = FALSE)
gp_set <- unique(edges$GP)
set.seed(1)
lay <- layout_with_fr(gi)
colnames(lay) <- c("x", "y"); rownames(lay) <- V(gi)$name
nm <- g %>% activate(nodes) %>% pull(name)
# highlight selected GPs: color their node + edges, label the genes they connect to
hl <- intersect(names(GP_HIGHLIGHTS), gp_set)
hl_genes <- setdiff(unique(unlist(
lapply(hl, function(gp) neighbors(gi, gp)$name))), gp_set)
g <- g %>% activate(nodes) %>% mutate(
is_gp = name %in% gp_set,
gp_fill = ifelse(is_gp & name %in% hl, unname(GP_HIGHLIGHTS[name]), GP_COL),
label_gene = !is_gp & name %in% hl_genes,
gene_lab = ifelse(label_gene, name, ""),
gp_size = ifelse(is_gp, 3, NA_real_))
g <- g %>% activate(edges) %>% mutate(
gp_end = ifelse(.N()$is_gp[from], .N()$name[from], .N()$name[to]),
gp_edge_highlight = gp_end %in% hl,
gp_edge_col = ifelse(gp_edge_highlight, unname(GP_HIGHLIGHTS[gp_end]), NA_character_))
p_1C <- ggraph(g, layout = "manual", x = lay[nm, "x"], y = lay[nm, "y"]) +
geom_edge_link(aes(filter = !gp_edge_highlight), color = "black", alpha = 0.18, width = 0.32) +
geom_edge_link(aes(filter = gp_edge_highlight, edge_colour = gp_edge_col), alpha = 0.95, width = 1.25) +
geom_node_point(aes(filter = !is_gp), shape = 16, size = 1, color = GENE_COL, alpha = 0.75) +
geom_node_point(aes(filter = is_gp, size = gp_size, fill = gp_fill),
shape = 21, color = "white", stroke = 0.5) +
geom_node_text(aes(filter = label_gene, label = gene_lab), repel = TRUE,
color = "black", size = 5, fontface = "italic", max.overlaps = Inf) +
scale_fill_identity() + scale_edge_colour_identity() +
scale_size_identity() + scale_edge_width_identity() +
scale_x_continuous(expand = expansion(mult = 0.08)) +
scale_y_continuous(expand = expansion(mult = 0.08)) +
theme_void(base_size = 12) +
theme(plot.margin = margin(10, 10, 10, 10), legend.position = "none")
ggsave(filename = paste0(figure_path, "1C.pdf"), plot = p_1C, width = 20, height = 20)
Fig. 1C. Gene-program (GP) network. A map of the 200
GPs and the signature genes they share. Each GP’s loadings are first
normalized within the GP (divided by that GP’s maximum absolute loading,
so loadings run 0–1), and every GP is connected to its top 5
up-regulated signature genes (largest positive normalized
loadings, ≥ 0.1). Grey balls are GPs and tan
balls are genes; an edge joins a GP to each of its top
signature genes, so a gene that appears among several GPs’ top genes
links those GPs, and a force-directed layout places programs with
overlapping signatures near one another. A selected set of GPs is
highlighted, each in its own color carried along its
edges to its (labeled) top signature genes: GP68 (pink), GP58 (orange),
GP35 (purple), GP171 (blue), GP1 (cyan), GP56 (red), GP161 (dark red),
GP6 (light green), GP7 (green), GP196 (yellow). This shows each
highlighted program’s signature and how programs that share genes sit
together — e.g. the Treg program GP68 (Foxp3, Il2ra, Izumo1r)
and the CD8 program GP58 (Cd8a, Cd8b1) occupy distinct
neighborhoods. An internal version with a color→GP legend and GP-index
labels is kept in experiments/gene_correlation_network/
(gp_highlight_internal).
(D) GP1 signature volcano
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_1D <- 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, "1D.pdf"), plot = p_1D, width = 7, height = 5.5)
Fig. 1D. “Signature volcano” plot for one example GP (GP1): each gene’s normalized score (x-axis, max |score| = 1) versus its mean shifted-log expression (y-axis), with the top-scoring genes labeled.
(E) Active-cell proportion 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)
pdf(paste0(figure_path, "hist_active_cells_per_GP.pdf"), width = 6, height = 4, useDingbats = FALSE)
hist(gp_active_cell_counts, breaks = 100, xlab = "Number of highly active cells per GP", main = "Histogram of highly active cells per GP", freq = TRUE)
dev.off()
gp_active_cell_prop <- gp_active_cell_counts / nrow(L_pm_norm_col)
p_1E <- ggplot(data.frame(prop = gp_active_cell_prop), aes(x = prop)) +
geom_histogram(bins = 40, fill = "steelblue", color = "white") +
scale_x_log10(labels = scales::label_percent()) +
annotation_logticks(sides = "b") +
labs(x = "Proportion of highly active cells per GP (log scale)", y = "Count",
title = "Histogram of highly active cells per GP (proportion)") +
theme_minimal(base_size = 13)
ggsave(filename = paste0(figure_path, "1E.pdf"), plot = p_1E, width = 6, height = 4, dpi = 300)
Fig. 1E. Histogram of the proportion of cells with high loading (> 0.1) per GP, across all 200 GPs (x-axis on log scale).
(F) Active-gene count 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_1F <- ggplot(data.frame(count = gp_active_gene_counts), aes(x = count)) +
geom_histogram(bins = 40, fill = "steelblue", color = "white") +
scale_x_log10(labels = scales::label_comma()) +
annotation_logticks(sides = "b") +
labs(x = "Number of highly active genes per GP (log scale)", y = "Count",
title = "Histogram of highly active genes per GP") +
theme_minimal(base_size = 13)
ggsave(filename = paste0(figure_path, "1F.pdf"), plot = p_1F, width = 6, height = 4, dpi = 300)
Fig. 1F. Histogram of the number of highly-active genes (|score| > 0.25 of that GP’s max) per GP (x-axis on log scale).
(G) Active-GP count by 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$cellID %in% cells_grp, , drop = FALSE]
data.frame(Group = grp, Active_Cell_Counts = rowSums(L_grp > 1e-1))
}))
group_counts <- gp_active_cell_counts_level1 %>%
group_by(Group) %>%
summarise(n = n(), .groups = "drop") %>%
mutate(Group_Label = paste0(Group, "\n(n=", n, ")")) %>%
mutate(Group = factor(Group, levels = level1_order)) %>%
arrange(Group) %>%
mutate(Group_Label = factor(Group_Label, levels = Group_Label))
plot_df_g <- gp_active_cell_counts_level1 %>% left_join(group_counts, by = "Group")
p_1G <- ggplot(plot_df_g, aes(x = Group_Label, y = Active_Cell_Counts, fill = Group)) +
geom_boxplot(outlier.size = 0.4, width = 0.6, alpha = 0.8, color = "gray40") +
scale_fill_manual(values = ZemmourLib::immgent_colors$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), plot.subtitle = element_text(size = 11, color = "gray30", 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, "1G.pdf"), plot = p_1G, width = 6, height = 4, dpi = 300) # boxplot_active_cells_per_GP_level1
Fig. 1G. Boxplot of the number of active GPs (loading > 0.1) per cell, grouped by major lineage.
(H) Active-GP count, activated vs. resting
cells_activated <- seurat_meta_filtered$cellID[seurat_meta_filtered$annotation_level2_group == "activated"]
L_pm_activated <- L_pm_filtered[seurat_meta_filtered$cellID %in% cells_activated, ]
gp_active_cell_counts_activated <- rowSums(L_pm_activated > 1e-1)
cells_resting <- seurat_meta_filtered$cellID[seurat_meta_filtered$annotation_level2_group == "resting"]
L_pm_resting <- L_pm_filtered[seurat_meta_filtered$cellID %in% cells_resting, ]
gp_active_cell_counts_resting <- rowSums(L_pm_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_1H <- 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, "1H.pdf"), plot = p_1H, width = 6, height = 4, dpi = 300) # boxplot_active_cells_per_GP
Fig. 1H. Boxplot of the number of active GPs per cell, comparing activated versus resting cells.
(I) CD44 vs. active-GP count
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)
set.seed(123)
df_nz <- gp_cd44_df %>% dplyr::filter(CD44_Protein_Level > 0)
df_nz <- df_nz %>% sample_n(min(10000, nrow(df_nz)))
R <- cor(df_nz$CD44_Protein_Level, df_nz$Active_GP_Counts, use = "complete.obs")
p_1I <- 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, "1I.pdf"), plot = p_1I, width = 6, height = 4, dpi = 300) # scatterplot_CD44_vs_active_GPs
Fig. 1I. Scatter plot of CD44 surface-protein level (log-normalized) against the number of active GPs per cell.
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: Asia/Shanghai
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