Figure 4: GPs associated with T cell activation
Ziang Zhang
Last updated: 2026-07-28
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Knit directory:
immgenT-GP-analysis/analysis/
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| 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 |
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| 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 | 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) |
| html | 6d1c6c0 | Ziang Zhang | 2026-07-04 | Fix TableS1 to use non-thymocyte (not healthy-restricted) AUC, matching published Table S1 |
| 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 |
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| 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/Figure4.R,
which shares its curated GP set with Figure
S3 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 4. GPs associated with T-cell activation.
#
# Panels produced. NOTE the renumbering: this figure's published counterpart is
# figures/Previous/bits/Figure *3*, panels 3c-3g (the old 3a/3b moved to
# Figure S3). Full caption text:
# ../immgen-t-factors/figures/Figure_Activation/Figure3_caption.md.
# 4a Standardized mean difference (d) in GP loading, activated vs resting,
# CD4 (x) vs CD8 (y); curated GPs colored by semantic group and labeled.
# 4b GP-gene signature network: each curated GP linked to its top 5
# positively/negatively regulated genes.
# 4c Bipartite TF-GP network for the curated activation GPs.
# 4d Heatmap of log2FC in mean GP loading across experimental conditions,
# for activated CD4/CD8 cells.
# 4e Heatmap of mean GP loading per Level-2 sub-lineage, across the 7
# T-cell lineages.
#
# Source: ported from Figure_Activation.R, which also produced the
# Figure S3 panels (see FigureS3.R) from the same curated GP set and cell
# groupings -- that shared setup now lives in
# code/R/activation_shared_setup.R, sourced by both scripts.
#
# 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 4/"
source("code/R/plot_utils.R") # scale_cols()
source("code/R/tf_network.R") # optimize_bipartite_order(), plot_tf_gp_network_v2()
# ============================================================
# 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")(4a) Activation effect, CD4 vs. CD8
# ============================================================
# 4a: 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_4a <- 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"
) +
# # TEMP: label the other auto-classified darkorange2 ("CD8 only") points too, just to eyeball them -- remove before final.
# ggrepel::geom_text_repel(
# data = filter(manual_curated_df, auto_color == "darkorange2", !(GP %in% GPs_of_interest)),
# aes(label = GP, color = point_color),
# max.overlaps = Inf, size = 3, box.padding = 0.3, point.padding = 0.4, segment.color = "grey70"
# ) +
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, "4a.pdf"),
plot = p_4a,
width = 8,
height = 7
)
Fig. 4a. Standardized mean difference (d) in GP activity between activated and resting cells in CD4+ T cells (x-axis) and CD8+ T cells (y-axis); for each GP, d is the activated-minus-resting difference in mean loading divided by that GP’s loading SD pooled over all activated and resting CD4/CD8 cells. Points are colored according to whether they are highly up-regulated in both CD4+ and CD8+ activation (dark red/brown), down-regulated in both (dark green), or preferentially regulated, in either direction, in CD4 (blue) or CD8 (orange). Remaining GPs are shown in black; the 25 activation-associated GPs discussed in the text are the curated set, and only those are labeled.
(4b) GP-gene signature network
# ============================================================
# 4b: 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_4b <- 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, "4b.pdf"),
plot = p_4b,
width = 10,
height = 10
)
Fig. 4b. GP-gene network in which each of these 25 GPs is linked to its five most strongly regulated genes, based on per-GP-normalized GP gene scores; edges are colored by the sign of the gene score (red, up-regulated; blue, down-regulated). GP nodes are colored as in (4a).
(4c) TF-GP network
# ============================================================
# 4c: Bipartite TF-GP network
# ============================================================
mm <- org.Mm.eg.db::org.Mm.eg.db
go2eg <- as.list(org.Mm.eg.db::org.Mm.egGO2ALLEGS)
tf_symbols <- AnnotationDbi::select(
mm,
keys = unique(unlist(go2eg)),
columns = "SYMBOL",
keytype = "ENTREZID"
)
tf <- c(
sort(tf_symbols$SYMBOL[
tf_symbols$ENTREZID %in% unique(go2eg[["GO:0003700"]])
]),
"Tox",
"Tox2",
"Tox3",
"Tox4"
) %>%
sort() %>%
unique()
F_sub_tf <- F_pm_filtered_norm[, GPs_of_interest, drop = FALSE]
tf_gp_threshold <- 0.25
tf_in_F <- intersect(tf, rownames(F_sub_tf))
tf_max_score <- apply(F_sub_tf[tf_in_F, , drop = FALSE], 1, max, na.rm = TRUE)
selected_tfs <- sort(names(tf_max_score)[tf_max_score > tf_gp_threshold])
# Top-to-bottom group order in the network: both-up -> CD8-only -> both-down -> CD4-only
gp_color_group_order <- c("darkred", "darkorange2", "darkgreen", "blue")
tf_network_plot <- plot_tf_gp_network_v2(
F = F_sub_tf,
selected_tfs = selected_tfs,
tf_gp_threshold = tf_gp_threshold,
top_genes_per_gp = 5,
gp_colors = highlight_colors,
gp_group_order = gp_color_group_order,
optimize_layout = TRUE,
barycenter_iter = 12,
gp_spacing = 1.5,
node_size_tf = 6,
node_size_gp = 5,
label_size_tf = 4.5,
label_size_gp = 4,
label_size_gene = 3.4
)
plot_height_tf <- min(
60,
max(12, length(selected_tfs) * 0.35, length(GPs_of_interest) * 1.5 * 0.55 + 2)
)
ggsave(
filename = paste0(figure_path, "4c.pdf"),
plot = tf_network_plot,
width = 18,
height = plot_height_tf,
limitsize = FALSE
)
Fig. 4c. Transcription factor (TF)-GP network: TFs (GO:0003700 DNA-binding TFs plus the Tox family) with a score greater than 0.25 in any of these 25 GPs are shown, connected to those GPs by directed edges whose width scales with the score (color identifies the TF). The top five up-regulated non-TF genes in each GP are listed beside the corresponding GP node (genes already drawn as TF nodes are excluded from that list).
(4d) Log2FC heatmap across conditions
# ============================================================
# 4d: log2FC heatmap of activated CD4+CD8 cells across conditions,
# relative to the per-GP mean across all CD4/CD8 cells
# ============================================================
act_keep <- seurat_meta_filtered$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$col <- row_label_cols
pdf(
paste0(figure_path, "4d.pdf"),
width = max(8, 0.18 * ncol(lfc_mat_mean) + 4),
height = 6
)
grid::grid.draw(ph_cond_lfc_mean$gtable)
invisible(dev.off())
Fig. 4d. 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 (resting and activated, all conditions) and capped at +-2, with a small pseudocount (1e-10) added to stabilize computation. Columns are conditions with >=50 activated CD4 and >=50 activated CD8 cells, grouped and annotated by broad condition category (including healthy controls), with color from purple (low) through black (no change) to gold (high). GPs are ordered and colored as in (4a).
(4e) Mean loading by sub-lineage
# ============================================================
# 4e: Mean GP loading per Level-2 sub-lineage (built before 4d since 4d
# reuses gp_row_order/group_colors computed here)
# ============================================================
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 4 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, "4e.pdf"), width = 11, height = 5.5)
grid::grid.draw(ph$gtable)
invisible(dev.off())
Fig. 4e. Heatmap of mean GP activity per level 2 cluster (sub-lineage), with the top bar denoting parent lineage; thymocytes and DP cells are excluded, leaving the seven T-cell lineages (CD8, CD4, Treg, gdT, CD8aa, Tz, DN) and only sub-types with >=50 cells. Color runs from white (low) to red (high). GPs are ordered and colored as in (4a).
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_1.8.7 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