Figure 5: Activated T cell states arise from shared and lineage-biased combinations of gene programs
Ziang Zhang
Last updated: 2026-09-10
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immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 4307b28 | Ziang Zhang | 2026-09-10 | New main Figure 4, and fold the cluster heatmap into Extended Data Figure 2 |
| html | a7a481f | Ziang Zhang | 2026-09-09 | Build site: the rebuilt Figure 1d and the Extended Data renumbering |
| 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 | 074dca1 | Ziang Zhang | 2026-08-05 | Build site: Extended Data tables reordered to six, internal notes off the pages |
| html | eeca07b | Ziang Zhang | 2026-08-05 | Keep pre-refactor provenance in panel comments off the published pages |
| html | cbcec52 | Ziang Zhang | 2026-07-30 | Build site: Extended Data Figure naming |
| html | ac650a0 | Ziang Zhang | 2026-07-30 | Build site: Figure S5 (ex-S6a) and Figure S6 as a-f |
| 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 | 620afca | Ziang Zhang | 2026-07-24 | Align Fig3/4/5 section titles with current numbers; reletter Fig4 to a-e |
| html | 620afca | Ziang Zhang | 2026-07-24 | Align Fig3/4/5 section titles with current numbers; reletter Fig4 to a-e |
| Rmd | 37db4e6 | Ziang Zhang | 2026-07-18 | Fig 5c: drop left expression heatmap, keep gene-score heatmap only |
| html | 37db4e6 | Ziang Zhang | 2026-07-18 | Fig 5c: drop left expression heatmap, keep gene-score heatmap only |
| Rmd | a18012d | Ziang Zhang | 2026-07-16 | Fig 5c: clip centered-expression color scale to [-1, 1] |
| html | a18012d | Ziang Zhang | 2026-07-16 | Fig 5c: clip centered-expression color scale to [-1, 1] |
| Rmd | 3577da1 | Ziang Zhang | 2026-07-16 | Fig 5c: center-only (no standardization), left panel as heatmap |
| html | 3577da1 | Ziang Zhang | 2026-07-16 | Fig 5c: center-only (no standardization), left panel as heatmap |
| Rmd | 2873ad2 | Ziang Zhang | 2026-07-16 | Unify Fig 3/4/5 panel filenames with new figure numbers |
| html | 2873ad2 | Ziang Zhang | 2026-07-16 | Unify Fig 3/4/5 panel filenames with new figure numbers |
| 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) |
All panels are produced by script/Figure5.R,
which shares its curated GP set with Extended
Data Figure 3 via code/R/activation_shared_setup.R. The
code below is shown for reference (not re-executed on this page); the
images are its pre-rendered output.
Setup
Data loading, shared across all panels below.
# Figure 5. GPs associated with T-cell activation.
#
# Panels produced:
# 5a Standardized mean difference (d) in GP loading, activated vs resting,
# CD4 (x) vs CD8 (y); curated GPs colored by semantic group and labeled.
# 5b GP-gene signature network: each curated GP linked to its top 5
# positively/negatively regulated genes.
# 5c Heatmap of log2FC in mean GP loading across experimental conditions,
# for activated CD4/CD8 cells.
# 5d Heatmap of mean GP loading per Level-2 sub-lineage, across the 7
# T-cell lineages.
#
# The curated GP set and activated/resting cell groupings are shared with
# Figure S3 via code/R/activation_shared_setup.R.
#
# Required inputs (data/) -- 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]
library(ggplot2)
library(ggrepel)
library(dplyr)
library(tidygraph)
library(ggraph)
library(pheatmap)
library(scales)
data_path <- "data/"
figure_path <- "figures/final-selected/Figure 5/"
source("code/R/plot_utils.R") # scale_cols()
# ============================================================
# Load data
# ============================================================
L_pm_filtered <- readRDS(paste0(data_path, "L_pm_filtered.rds"))
colnames(L_pm_filtered) <- paste0("GP", seq_len(ncol(L_pm_filtered)))
F_pm_filtered <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
colnames(F_pm_filtered) <- paste0("GP", seq_len(ncol(F_pm_filtered)))
seurat_meta <- readRDS(paste0(
data_path,
"igt1_96_withtotalvi20260206_clean_ADTonly.Rds"
))@meta.data
seurat_meta_filtered <- seurat_meta[rownames(L_pm_filtered), ]
source("code/R/activation_shared_setup.R")(a) Activation effect, CD4 vs. CD8
# ============================================================
# 5a: Standardized mean difference, activated vs resting, CD4 vs CD8
# ============================================================
d_thr <- 0.15
ratio_cutoff <- 3
GP_activation_summary <- diff_factors_merged %>%
dplyr::inner_join(
d_factors_merged %>% dplyr::select(SYMBOL, d_CD4, d_CD8),
by = "SYMBOL"
) %>%
dplyr::mutate(
Ratio_CD8_CD4 = mean_change_loadings_CD8 / mean_change_loadings_CD4
) %>%
dplyr::select(
GP = SYMBOL,
mean_change_loadings_CD4,
mean_change_loadings_CD8,
AveExpr_CD4,
AveExpr_CD8,
d_CD4,
d_CD8,
Ratio_CD8_CD4
)
# Colour every GP using the same four-category rule (ratio + sign + magnitude
# gate via d_thr). Curated GPs (GPs_of_interest) override with their fixed
# manual highlight_colors; non-curated GPs are classified automatically.
# Only the curated GPs are labelled, to keep the plot readable.
manual_curated_df <- GP_activation_summary %>%
dplyr::mutate(
auto_color = dplyr::case_when(
abs(Ratio_CD8_CD4) > ratio_cutoff & abs(d_CD8) > d_thr ~ "darkorange2",
abs(Ratio_CD8_CD4) < 1 / ratio_cutoff & abs(d_CD4) > d_thr ~ "blue",
abs(Ratio_CD8_CD4) > 1 / ratio_cutoff &
abs(Ratio_CD8_CD4) < ratio_cutoff &
d_CD4 > d_thr &
d_CD8 > d_thr ~ "darkred",
abs(Ratio_CD8_CD4) > 1 / ratio_cutoff &
abs(Ratio_CD8_CD4) < ratio_cutoff &
d_CD4 < -d_thr &
d_CD8 < -d_thr ~ "darkgreen",
TRUE ~ "black"
),
point_color = ifelse(
GP %in% GPs_of_interest,
highlight_colors[GP],
auto_color
)
)
p_5a <- ggplot(manual_curated_df, aes(x = d_CD4, y = d_CD8)) +
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(aes(color = point_color), size = 2) +
ggrepel::geom_text_repel(
seed = 42,
data = filter(manual_curated_df, GP %in% GPs_of_interest),
aes(label = GP, color = point_color),
max.overlaps = Inf,
size = 3.5,
box.padding = 0.35,
point.padding = 0.5,
segment.color = "grey50"
) +
scale_color_identity() +
# Signed (pseudo-)log axes: d is signed, so a plain log drops negatives/zeros.
scale_x_continuous(
trans = scales::pseudo_log_trans(sigma = 0.15),
breaks = c(-1, -0.5, -0.2, -0.1, 0, 0.1, 0.2, 0.5, 1)
) +
scale_y_continuous(
trans = scales::pseudo_log_trans(sigma = 0.15),
breaks = c(-1, -0.5, -0.2, -0.1, 0, 0.1, 0.2, 0.5, 1)
) +
coord_equal(xlim = c(-1.6, 1.6), ylim = c(-1.6, 1.6)) +
labs(
x = "Standardized Mean Difference (d) for CD4 Activated vs Resting",
y = "Standardized Mean Difference (d) for CD8 Activated vs Resting",
title = "GPs colored by semantic category (curated set labeled)"
) +
theme_minimal()
ggsave(
filename = paste0(figure_path, "5a.pdf"),
plot = p_5a,
width = 8,
height = 7
)
Fig. 5a. Standardized mean difference in GP activity between activated and resting cells in CD4+ T cells (x-axis) and CD8+ T cells (y-axis). Points are colored according to whether they are highly upregulated in both CD4+ and CD8+ activation (brown), downregulated in both (green), or preferentially upregulated in CD4 (blue) or CD8 (orange).
(b) GP-gene signature network
# ============================================================
# 5b: GP-gene signature network
# ============================================================
set.seed(42)
F_pm_filtered_norm_subset <- F_pm_filtered_norm[, GPs_of_interest, drop = FALSE]
top_5_pos <- apply(F_pm_filtered_norm_subset, 2, function(x) {
idx <- order(abs(x), decreasing = TRUE)[1:5]
idx <- idx[x[idx] > 0]
names(x)[idx]
})
top_5_neg <- apply(F_pm_filtered_norm_subset, 2, function(x) {
idx <- order(abs(x), decreasing = TRUE)[1:5]
idx <- idx[x[idx] < 0]
names(x)[idx]
})
names(top_5_pos) <- GPs_of_interest
names(top_5_neg) <- GPs_of_interest
pos_edges <- stack(top_5_pos) %>%
dplyr::rename(Gene = values, GP = ind) %>%
dplyr::mutate(Type = "Positive", Color = "red")
neg_edges <- stack(top_5_neg) %>%
dplyr::rename(Gene = values, GP = ind) %>%
dplyr::mutate(Type = "Negative", Color = "blue")
all_edges <- dplyr::bind_rows(pos_edges, neg_edges) %>%
dplyr::filter(Gene != "" & !is.na(Gene))
all_edges_sorted <- all_edges %>% dplyr::arrange(Type, GP, Gene)
gp_group_df <- data.frame(
name = names(highlight_colors),
ManualGroup = dplyr::case_when(
highlight_colors == "blue" ~ "CD4 only",
highlight_colors == "darkorange2" ~ "CD8 only",
highlight_colors == "darkgreen" ~ "both down",
highlight_colors == "darkred" ~ "both up",
)
)
manual_colors_palette <- c(
"CD4 only" = "blue",
"CD8 only" = "darkorange2",
"both down" = "darkgreen",
"both up" = "darkred",
"Gene" = "#666666"
)
graph <- tidygraph::as_tbl_graph(all_edges_sorted) %>%
tidygraph::activate(nodes) %>%
dplyr::mutate(
NodeGroup = ifelse(name %in% all_edges$GP, "GP", "Gene"),
Importance = tidygraph::centrality_degree()
) %>%
dplyr::left_join(gp_group_df, by = "name") %>%
dplyr::mutate(
ColorGroup = ifelse(NodeGroup == "Gene", "Gene", ManualGroup),
gp_label = ifelse(NodeGroup == "GP", name, "")
)
set.seed(2)
p_5b <- ggraph(graph, layout = "stress") +
geom_edge_link(aes(color = Color), alpha = 0.4, width = 0.6) +
geom_node_point(
aes(filter = (NodeGroup == "Gene"), color = ColorGroup),
shape = 16,
size = 2,
alpha = 0.8
) +
geom_node_point(
aes(filter = (NodeGroup == "GP"), color = ColorGroup),
shape = 15,
size = 10,
alpha = 0.7
) +
geom_node_text(
aes(filter = (NodeGroup == "GP"), label = gp_label),
color = "white",
fontface = "bold",
size = 3
) +
geom_node_text(
aes(filter = (NodeGroup == "Gene"), label = name),
repel = TRUE,
size = 2.5,
color = "black",
max.overlaps = 20
) +
scale_edge_color_identity() +
scale_color_manual(
name = "GP Types",
values = manual_colors_palette,
breaks = c("CD4 only", "CD8 only", "both down", "both up")
) +
theme_void() +
labs(
title = "GP-Gene Signature Network",
subtitle = "Nodes colored by manual GP classification",
caption = "Red edges: Positive | Blue edges: Negative"
) +
theme(
legend.position = "bottom",
legend.title = element_text(face = "bold"),
plot.margin = margin(10, 10, 10, 10)
) +
guides(color = guide_legend(override.aes = list(size = 5, shape = 15)))
ggsave(
filename = paste0(figure_path, "5b.pdf"),
plot = p_5b,
width = 10,
height = 10
)
Fig. 5b. GP-gene network in which each GP is linked to its five most strongly regulated genes, based on GP gene scores. Edges are colored by the sign of the gene score (red for upregulated, blue for downregulated). GP nodes are colored as in (a).
(c) Log2FC heatmap across conditions
# ============================================================
# 5c: log2FC heatmap of activated CD4+CD8 cells across conditions,
# relative to the per-GP mean across all CD4/CD8 cells
# ============================================================
act_keep <- seurat_meta_filtered$annotation_level1 %in%
c("CD4", "CD8") &
seurat_meta_filtered$annotation_level2_group == "activated"
meta_act <- seurat_meta_filtered[act_keep, ]
L_act <- L_subset[act_keep, , drop = FALSE]
min_cells_cond <- 50
cd_lin <- table(
meta_act$condition_detailed_simplified,
meta_act$annotation_level1
)
cond_keep <- rownames(cd_lin)[
cd_lin[, "CD4"] >= min_cells_cond & cd_lin[, "CD8"] >= min_cells_cond
]
cd_br <- table(meta_act$condition_detailed_simplified, meta_act$condition_broad)
cd_to_broad <- setNames(
colnames(cd_br)[apply(cd_br, 1, which.max)],
rownames(cd_br)
)[cond_keep]
# Column order: `healthy` broad first (with `baseline` as its first condition)
broad_rank <- ifelse(cd_to_broad == "healthy", 0L, 1L)
within_broad_rank <- ifelse(
cd_to_broad == "healthy" & cond_keep == "baseline",
0L,
1L
)
col_order_cond <- order(broad_rank, cd_to_broad, within_broad_rank, cond_keep)
cond_keep <- cond_keep[col_order_cond]
cd_to_broad <- cd_to_broad[cond_keep]
mean_mat_cond <- vapply(
cond_keep,
function(cond) {
colMeans(L_act[
meta_act$condition_detailed_simplified == cond,
,
drop = FALSE
])
},
numeric(ncol(L_act))
)
mean_mat_cond <- mean_mat_cond[gp_row_order, , drop = FALSE]
broad_levels <- unique(cd_to_broad)
col_anno_cond <- data.frame(
condition_broad = factor(cd_to_broad, levels = broad_levels),
row.names = colnames(mean_mat_cond)
)
row_label_cols <- group_colors[gp_to_group[gp_row_order]]
pc_lfc <- 1e-10
cap_lfc <- 2
cd4cd8_idx <- seurat_meta_filtered$annotation_level1 %in% c("CD4", "CD8")
L_cd4cd8 <- L_subset[cd4cd8_idx, , drop = FALSE]
mu_lfc_mean <- colMeans(L_cd4cd8, na.rm = TRUE)[rownames(mean_mat_cond)]
lfc_mat_mean <- log2((mean_mat_cond + pc_lfc) / (mu_lfc_mean + pc_lfc))
lfc_mat_mean <- pmax(pmin(lfc_mat_mean, cap_lfc), -cap_lfc)
ph_cond_lfc_mean <- pheatmap(
lfc_mat_mean,
cluster_rows = FALSE,
cluster_cols = FALSE,
color = colorRampPalette(c("#7A0177", "black", "#FFD700"))(101),
breaks = seq(-cap_lfc, cap_lfc, length.out = 102),
annotation_col = col_anno_cond,
gaps_row = head(cumsum(lengths(gp_groups)), -1),
gaps_col = head(cumsum(rle(as.character(cd_to_broad))$lengths), -1),
main = "log2FC vs per-GP MEAN across all CD4/CD8 (activated CD4+CD8 by condition_detailed_simplified)",
silent = TRUE
)
row_idx_lfc_m <- which(ph_cond_lfc_mean$gtable$layout$name == "row_names")
ph_cond_lfc_mean$gtable$grobs[[row_idx_lfc_m]]$gp$col <- row_label_cols
pdf(
paste0(figure_path, "5c.pdf"),
width = max(8, 0.18 * ncol(lfc_mat_mean) + 4),
height = 6
)
grid::grid.draw(ph_cond_lfc_mean$gtable)
invisible(dev.off())
Fig. 5c. Heatmap of log2 fold change in mean GP activity across experimental conditions for activated CD4+ and CD8+ T cells, computed relative to each GP’s mean loading across all CD4+/CD8+ cells. Columns grouped and annotated by immune challenge category. GPs are ordered and colored as in (a).
(d) Mean loading by sub-lineage
# ============================================================
# 5d: Mean GP loading per Level-2 sub-lineage
# ============================================================
keep_cells <- seurat_meta_filtered$annotation_level1 %in% lineages
meta_sub <- seurat_meta_filtered[keep_cells, ]
L_keep <- L_subset[keep_cells, , drop = FALSE]
l2_counts <- table(meta_sub$annotation_level2)
l2_keep <- names(l2_counts)[l2_counts >= 50]
# Drop the "P" cluster and any "w..." clusters (wM, wW, etc.) across all lineages
l2_stripped <- sub("^[^._]+[._]", "", l2_keep)
exclude_l2 <- l2_stripped == "P" |
grepl("^w", l2_stripped, ignore.case = TRUE) |
grepl("[._]w", l2_keep, ignore.case = TRUE)
l2_keep <- l2_keep[!exclude_l2]
mean_mat <- vapply(
l2_keep,
function(l2) {
colMeans(L_keep[meta_sub$annotation_level2 == l2, , drop = FALSE])
},
numeric(ncol(L_keep))
)
l2_to_l1 <- vapply(
l2_keep,
function(l2) {
as.character(meta_sub$annotation_level1[meta_sub$annotation_level2 == l2][
1
])
},
character(1)
)
col_order <- order(match(l2_to_l1, lineages), l2_keep)
mean_mat <- mean_mat[gp_row_order, col_order]
l2_to_l1 <- l2_to_l1[col_order]
immgen_cols <- ZemmourLib::immgent_colors
col_anno <- data.frame(
Lineage = factor(l2_to_l1, levels = lineages),
row.names = colnames(mean_mat)
)
anno_colors_mean <- list(Lineage = immgen_cols$level1[lineages])
row_label_cols <- group_colors[gp_to_group[gp_row_order]]
col_label_cols <- immgen_cols$level2[colnames(mean_mat)]
col_label_cols[is.na(col_label_cols)] <- "black"
ph <- pheatmap(
mean_mat,
cluster_rows = FALSE,
cluster_cols = FALSE,
color = colorRampPalette(c("white", "red"))(200),
annotation_col = col_anno,
annotation_colors = anno_colors_mean,
gaps_row = head(cumsum(lengths(gp_groups)), -1),
gaps_col = head(cumsum(rle(l2_to_l1)$lengths), -1),
main = "Average loading of Figure 5 GPs per Level-2 sub-lineage",
silent = TRUE
)
row_idx <- which(ph$gtable$layout$name == "row_names")
col_idx <- which(ph$gtable$layout$name == "col_names")
ph$gtable$grobs[[row_idx]]$gp$col <- row_label_cols
ph$gtable$grobs[[col_idx]]$gp$col <- col_label_cols
pdf(paste0(figure_path, "5d.pdf"), width = 11, height = 5.5)
grid::grid.draw(ph$gtable)
invisible(dev.off())
Fig. 5d. Heatmap of mean GP activity per level 2 cluster, with the top bar denoting parent lineage. GP ordered and colored as in (a).
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