Figure 1: Overview
Ziang Zhang
Last updated: 2026-07-03
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Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8ac7f9f | Ziang Zhang | 2026-07-03 | 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 C and 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)
| Version | Author | Date |
|---|---|---|
| 06b2461 | Ziang Zhang | 2026-07-02 |
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) GP loading heatmap
# ============================================================
# 1C: giant heatmap of 200 GP loadings, cells stratified by lineage x organ
# ============================================================
set.seed(6173)
MIN_CELLS <- 20
N_SAMPLE <- 80
TOP_ORGANS <- 5
K_ANCHOR <- 5
level1_order <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN")
organs_all <- as.character(unique(seurat_meta_filtered$organ_simplified))
ln_match <- organs_all[grepl("^LN$|lymph", organs_all, ignore.case = TRUE)]
spleen_match <- organs_all[grepl("spleen", organs_all, ignore.case = TRUE)]
other_organs <- sort(setdiff(organs_all, c(ln_match, spleen_match)))
organ_order <- c(spleen_match, ln_match, other_organs)
top_organ_combos <- seurat_meta_filtered |>
dplyr::filter(annotation_level1 %in% level1_order) |>
dplyr::count(annotation_level1, organ_simplified) |>
dplyr::group_by(annotation_level1) |>
dplyr::slice_max(n, n = TOP_ORGANS, with_ties = FALSE) |>
dplyr::ungroup() |>
dplyr::select(annotation_level1, organ_simplified)
sampled_random <- seurat_meta_filtered |>
dplyr::filter(annotation_level1 %in% level1_order) |>
dplyr::inner_join(top_organ_combos, by = c("annotation_level1", "organ_simplified")) |>
dplyr::group_by(annotation_level1, organ_simplified) |>
dplyr::filter(dplyr::n() >= MIN_CELLS) |>
dplyr::slice_sample(n = N_SAMPLE) |>
dplyr::ungroup()
anchor_cellids <- apply(L_pm_filtered, 2, function(x) {
rownames(L_pm_filtered)[order(x, decreasing = TRUE)[seq_len(K_ANCHOR)]]
}) |> as.vector() |> unique()
anchor_meta <- seurat_meta_filtered |>
dplyr::filter(cellID %in% anchor_cellids, annotation_level1 %in% level1_order) |>
dplyr::inner_join(top_organ_combos, by = c("annotation_level1", "organ_simplified"))
all_meta <- dplyr::bind_rows(sampled_random, anchor_meta) |>
dplyr::distinct(cellID, .keep_all = TRUE) |>
dplyr::arrange(factor(annotation_level1, levels = level1_order), factor(organ_simplified, levels = organ_order))
L_sampled <- L_pm_filtered[all_meta$cellID, ]
clip_val <- quantile(L_sampled, 0.99)
L_display <- pmin(L_sampled, clip_val)
colnames(L_display) <- gsub("^K", "GP", colnames(L_display))
col_fun <- colorRamp2(c(0, clip_val / 2, clip_val), c("white", "#4393c3", "#08306b"))
level1_colors <- ZemmourLib::immgent_colors$level1
organ_colors <- ZemmourLib::immgent_colors$organ_simplified
level1_present <- intersect(level1_order, as.character(unique(all_meta$annotation_level1)))
organ_present <- intersect(organ_order, as.character(unique(all_meta$organ_simplified)))
row_ann <- rowAnnotation(
Cell_Type = factor(as.character(all_meta$annotation_level1), levels = level1_present),
Organ = factor(as.character(all_meta$organ_simplified), levels = organ_present),
col = list(Cell_Type = level1_colors[level1_present], Organ = organ_colors[organ_present]),
annotation_name_gp = gpar(fontsize = 8),
annotation_legend_param = list(Cell_Type = list(title = "Cell Type"), Organ = list(title = "Organ"))
)
ht <- Heatmap(
L_display, name = "Loading", col = col_fun, left_annotation = row_ann,
cluster_rows = FALSE, cluster_columns = TRUE,
clustering_distance_columns = "euclidean", clustering_method_columns = "ward.D2",
show_row_names = FALSE, column_names_gp = gpar(fontsize = 4),
column_title = "Gene Programs (GPs)", column_title_gp = gpar(fontsize = 11, fontface = "bold"),
use_raster = TRUE, raster_quality = 3, border = FALSE,
heatmap_legend_param = list(title = "Loading", direction = "vertical")
)
pdf(paste0(figure_path, "1C.pdf"), width = 15, height = 20, useDingbats = FALSE)
draw(ht, merge_legend = TRUE)
dev.off()
| Version | Author | Date |
|---|---|---|
| 06b2461 | Ziang Zhang | 2026-07-02 |
Fig. 1C. Heatmap of all 200 GP loadings across a stratified sample of cells, ordered by lineage then organ; columns (GPs) are clustered by similarity.
(D) GP1 signature volcano
# ============================================================
# 1D: GP1 signature volcano (see code/R/volcano_helpers.R header
# for why this isn't from Figure_Overview.R)
# ============================================================
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)
| Version | Author | Date |
|---|---|---|
| 06b2461 | Ziang Zhang | 2026-07-02 |
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)
pdf(paste0(figure_path, "1E.pdf"), width = 6, height = 4, useDingbats = FALSE) # hist_active_cells_prop_per_GP
hist(gp_active_cell_prop, breaks = 100, xlab = "Proportion of highly active cells per GP", main = "Histogram of highly active cells per GP (proportion)", freq = TRUE)
dev.off()
| Version | Author | Date |
|---|---|---|
| 06b2461 | Ziang Zhang | 2026-07-02 |
Fig. 1E. Histogram of the proportion of cells with high loading (> 0.1) per GP, across all 200 GPs.
(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)
pdf(paste0(figure_path, "1F.pdf"), width = 6, height = 4, useDingbats = FALSE) # hist_active_genes_per_GP
hist(gp_active_gene_counts, breaks = 100, xlab = "Number of highly active genes per GP", main = "Histogram of highly active genes per GP", freq = TRUE)
dev.off()
gp_active_gene_prop <- gp_active_gene_counts / nrow(F_pm_norm_col)
pdf(paste0(figure_path, "hist_active_genes_prop_per_GP.pdf"), width = 6, height = 4, useDingbats = FALSE)
hist(gp_active_gene_prop, breaks = 100, xlab = "Proportion of highly active genes per GP", main = "Histogram of highly active genes per GP (proportion)", freq = TRUE)
dev.off()
| Version | Author | Date |
|---|---|---|
| 06b2461 | Ziang Zhang | 2026-07-02 |
Fig. 1F. Histogram of the number of highly-active genes (|score| > 0.25 of that GP’s max) per GP.
(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$level1) +
labs(title = "Active Gene Programs per Group", x = "Cell Group (Annotation Level 1)", y = "Number of highly active GPs") +
theme_minimal(base_size = 13) +
theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5), plot.subtitle = element_text(size = 11, color = "gray30", hjust = 0.5), axis.text.x = element_text(size = 11, angle = 45, hjust = 1), axis.text.y = element_text(size = 12), axis.title.y = element_text(size = 13, face = "bold"), legend.position = "none", panel.grid.minor = element_blank())
ggsave(filename = paste0(figure_path, "1G.pdf"), plot = p_1G, width = 6, height = 4, dpi = 300) # boxplot_active_cells_per_GP_level1
| Version | Author | Date |
|---|---|---|
| 06b2461 | Ziang Zhang | 2026-07-02 |
Fig. 1G. Boxplot of the number of active GPs (loading > 0.1) per cell, grouped by major lineage.
(H) Active-GP count, activated vs. resting
cells_activated <- seurat_meta_filtered$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$cellID %in% cells_resting, ]
gp_active_cell_counts_resting <- rowSums(L_pm_resting > 1e-1)
gp_active_cell_counts_df <- data.frame(
Group = c(rep("Activated", length(gp_active_cell_counts_activated)), rep("Resting", length(gp_active_cell_counts_resting))),
Active_Cell_Counts = c(gp_active_cell_counts_activated, gp_active_cell_counts_resting)
)
p_1H <- ggplot(gp_active_cell_counts_df, aes(x = Group, y = Active_Cell_Counts, fill = Group)) +
geom_boxplot(outlier.size = 0.4, width = 0.6, alpha = 0.8, color = "gray40") +
scale_fill_manual(values = c("Activated" = "#1f78b4", "Resting" = "#e31a1c")) +
labs(title = "", x = "", y = "Number of highly active GPs") +
theme_minimal(base_size = 13) +
theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5), axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12), axis.title.y = element_text(size = 13, face = "bold"), legend.position = "none")
ggsave(filename = paste0(figure_path, "1H.pdf"), plot = p_1H, width = 6, height = 4, dpi = 300) # boxplot_active_cells_per_GP
| Version | Author | Date |
|---|---|---|
| 06b2461 | Ziang Zhang | 2026-07-02 |
Fig. 1H. Boxplot of the number of active GPs per cell, comparing activated versus resting cells.
(I) CD44 vs. active-GP count
gp_active_cell_counts_all <- rowSums(L_pm_filtered > 1e-1)
gp_cd44_df <- data.frame(CD44_Protein_Level = protein_mat_normalized_lognorm, Active_GP_Counts = gp_active_cell_counts_all)
set.seed(123)
df_nz <- gp_cd44_df %>% dplyr::filter(CD44_Protein_Level > 0)
df_nz <- df_nz %>% sample_n(min(10000, nrow(df_nz)))
R <- cor(df_nz$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
| Version | Author | Date |
|---|---|---|
| 06b2461 | Ziang Zhang | 2026-07-02 |
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/Tokyo
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