Figure 6: Linking GPs to proteins
Ziang Zhang
Last updated: 2026-07-27
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Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
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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 | ffe285c | Ziang Zhang | 2026-07-27 | Reorganize figures/ and untrack local-only exploration notes |
| 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 | 06552b8 | Ziang Zhang | 2026-07-14 | Update Figure 6b heatmap layout |
| html | 06552b8 | Ziang Zhang | 2026-07-14 | Update Figure 6b heatmap layout |
| html | 92021bf | Ziang Zhang | 2026-07-02 | Build site. |
| Rmd | db7a5cd | Ziang Zhang | 2026-07-02 | Recover 3 data-provenance gaps into pipeline scripts |
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| Rmd | 8ac7f9f | Ziang Zhang | 2026-07-02 | Add data provenance notes to each script; remove conversational |
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| 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 (a) are produced by script/Figure6.R,
which shares its CITE-seq setup with Figure
S6 via code/R/citeseq_shared_setup.R. The code below is
shown for reference (not re-executed on this page, since this script
takes about a minute to run); the images are its pre-rendered
output.
Setup
library(ggplot2)
library(ggrepel)
library(dplyr)
library(patchwork)
library(pheatmap)
library(tidyr)
library(Matrix) # protein matrices are dgCMatrix; must be attached for `[` to dispatch
data_path <- "data/"
figure_path <- "figures/final-selected/Figure 6/"
source("code/R/gated_protein_helpers.R")
# 6a: hand-drawn schematic -- not code-generated, no output here.Panels (b) onward additionally load the shared CITE-seq cell/protein setup:
# ============================================================
# Load data (shared with FigureS6.R)
# ============================================================
source("code/R/citeseq_shared_setup.R")
# Re-derive the normalized protein matrix used only by this script's panel
# 6b (Protein_F_pm_raw filtered/scaled -- FigureS6.R doesn't need it).
Protein_F_pm <- Protein_F_pm_raw[!rownames(Protein_F_pm_raw) %in% isotype_proteins, ]
Protein_F_pm <- Protein_F_pm[rownames(Protein_F_pm) %in% good_proteins, ]
Protein_F_pm <- Protein_F_pm[!rownames(Protein_F_pm) %in% exclude_proteins, ]
Protein_F_pm <- Protein_F_pm[!rownames(Protein_F_pm) %in% thy11_proteins, ]
D_lognorm <- diag(1 / apply(Protein_F_pm, 2, function(x) max(abs(x))))
Protein_F_pm <- Protein_F_pm %*% D_lognorm
colnames(Protein_F_pm) <- paste0("GP", 1:ncol(Protein_F_pm))
Protein_F_pm[is.na(Protein_F_pm)] <- 0(a) Projection 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/Previous/bits/Figure 6/6a.pdf for the published
panel.
(b) Protein-program heatmap
# ============================================================
# 6b: sparse protein-program heatmap, contamination GPs removed
# ============================================================
threshold_simplified <- 0
keep_rows_simplified <- apply(Protein_F_pm, 1, function(v) any(abs(v) > threshold_simplified, na.rm = TRUE))
Protein_F_pm_simplified <- Protein_F_pm[keep_rows_simplified, , drop = FALSE]
keep_cols_simplified <- apply(Protein_F_pm_simplified, 2, function(v) any(abs(v) > threshold_simplified, na.rm = TRUE))
Protein_F_pm_simplified <- Protein_F_pm_simplified[, keep_cols_simplified, drop = FALSE]
GP_contamination <- c("GP40", "GP50", "GP55", "GP188")
Protein_F_pm_simplified_no_contamination <- Protein_F_pm_simplified[, !colnames(Protein_F_pm_simplified) %in% GP_contamination, drop = FALSE]
sparse_cutoff <- 0.5
bk_sparse <- unique(c(seq(-1, -sparse_cutoff, length.out = 26), seq(-sparse_cutoff, sparse_cutoff, length.out = 51), seq(sparse_cutoff, 1, length.out = 26)))
cols_sparse <- c(colorRampPalette(c("#4575B4", "white"))(25), rep("white", 50), colorRampPalette(c("white", "#D73027"))(25))
# Display proteins as rows and GPs as columns. Order GP columns from most to
# fewest visible proteins. Order protein rows by their rightmost visible GP, so
# proteins extending into the sparse right side appear first and form a
# triangular boundary. Visible count and a rarity-weighted support score provide
# deterministic secondary ordering.
wide_matrix_6b <- as.matrix(Protein_F_pm_simplified_no_contamination)
wide_visible_mask_6b <- abs(wide_matrix_6b) >= sparse_cutoff
wide_gp_visible_count_6b <- colSums(wide_visible_mask_6b)
wide_protein_visible_count_6b <- rowSums(wide_visible_mask_6b)
wide_gp_number_6b <- as.integer(sub("^GP", "", colnames(wide_matrix_6b)))
wide_gp_order_6b <- order(-wide_gp_visible_count_6b, wide_gp_number_6b)
wide_mask_ordered_cols_6b <- wide_visible_mask_6b[
,
wide_gp_order_6b,
drop = FALSE
]
wide_rightmost_visible_gp_6b <- apply(
wide_mask_ordered_cols_6b,
1,
function(values) max(which(values))
)
wide_rarity_weights_6b <- seq_len(ncol(wide_mask_ordered_cols_6b))^2
wide_protein_rarity_score_6b <- as.numeric(
wide_mask_ordered_cols_6b %*% wide_rarity_weights_6b
)
wide_protein_order_6b <- order(
-wide_rightmost_visible_gp_6b,
-wide_protein_visible_count_6b,
-wide_protein_rarity_score_6b,
rownames(wide_matrix_6b)
)
wide_ordered_matrix_6b <- wide_matrix_6b[
wide_protein_order_6b,
wide_gp_order_6b,
drop = FALSE
]
pdf(paste0(figure_path, "6b.pdf"), width = 48, height = 14)
pheatmap::pheatmap(
wide_ordered_matrix_6b,
main = sprintf(
paste0(
"Protein programs - GP columns, triangular-first protein rows ",
"(|score| >= %.1f; protein-row sparsity is not monotone)"
),
sparse_cutoff
),
color = cols_sparse,
breaks = bk_sparse,
cluster_rows = FALSE,
cluster_cols = FALSE,
border_color = "grey75",
fontsize = 16,
fontsize_row = 16,
fontsize_col = 16,
angle_col = 90,
legend_breaks = c(-1, -sparse_cutoff, 0, sparse_cutoff, 1),
legend_labels = c("-1", "-0.5", "0 (white)", "0.5", "1")
)
dev.off()
Fig. 6b. Heatmap of the re-estimated protein matrix (U). The protein scores in each GP are scaled so its maximum |score| = 1, after removing isotype and low-quality proteins and four putative contamination GPs (GP40, GP50, GP55, GP188); 47 proteins (rows) x 179 GPs (columns). Entries with |score| < 0.5 are shown white, with color running from blue (-1) through white to red (+1). GP columns are ordered from the most to the fewest visible proteins. Protein rows are ordered by the position of their rightmost visible GP, placing proteins that extend into the sparse right side first and exposing a triangular support boundary; visible count and a rarity-weighted support score break ties. This triangular-first row order is not monotone in per-protein sparsity. GP labels are vertical and sized to the available heatmap-cell width; protein labels are sized to the available row height.
(c-f) Protein-gated vs. GP-loading populations
# ============================================================
# 6c-6f: protein-gate vs. GP-loading comparison for the 4 curated main-figure GPs
# (df_markers2, thymocyte/proliferating/miniverse_cells, L_pm_for_gating,
# select_proteins, threshold_results_subset_manual all come from
# citeseq_shared_setup.R above)
# ============================================================
# Panel lettering follows the published figure: c = GP171, d = GP12, e = GP80,
# f = GP23. (It is NOT the order the GPs happen to be listed in below -- an
# earlier version of this script assigned the letters positionally, which
# silently swapped d/e/f relative to the published panels.)
GPs_fig6 <- c("GP171", "GP23", "GP12", "GP80")
fig6_letter <- c("GP171" = "6c", "GP12" = "6d", "GP80" = "6e", "GP23" = "6f")
enlarge_gps <- c("GP8", "GP30", "GP170", "GP107")
for (gp in GPs_fig6) {
k_name <- paste0("K", sub("^GP", "", gp))
plot_gated_gp_vs_protein(
gp_name = k_name,
df_markers = df_markers2,
protein_mat = protein_mat_normalized_lognorm,
loading_mat = L_pm_for_gating,
mde_emb = mde_result,
missing_threshold_action = "skip",
threshold_df = threshold_results_subset_manual,
exclude_cells = c(thymocyte_cells, proliferating_cells, miniverse_cells),
selected_proteins = select_proteins,
loading_q = NULL,
min_pointsize = if (gp %in% enlarge_gps) 3L else 0L,
save_path = paste0(figure_path, fig6_letter[gp], ".pdf")
)
}
Fig. 6c-f. Transcriptional GPs recover protein-gated populations. For each GP, cells are shown twice on the same MDE embedding. Left, cells passing a protein gate built from the GP’s curated marker signature (positive markers above, and negative markers below); right, an equally sized set of cells with the highest GP loading (the loading cutoff is chosen to match the protein-gate count). Highlighted cells are colored by two-dimensional density and all other cells are grey; thymocytes, proliferating, and “miniverse” cells are excluded.
(g, h) KLRG1 modulation across lineages
# ============================================================
# 6g/6h: KLRG1 modulation (CD8 vs CD4, CD8 vs Treg)
# ============================================================
FlashierDGE_corrected <- function(F1, L1, group1, group2, title_plot = "") {
loadings_group1 <- colMeans(L1[group1, ])
loadings_group2 <- colMeans(L1[group2, ])
mean_change_loadings <- loadings_group1 - loadings_group2
vplot <- data.frame(SYMBOL = names(mean_change_loadings), mean_change_loadings = mean_change_loadings, AveExpr = colMeans(L1[c(group1, group2), ]))
list(diff_factors = vplot)
}
get_klrg1_split <- function(cell_type_label, meta, protein_data, threshold) {
cells <- meta$cellID[meta$annotation_level1 == cell_type_label]
cells <- intersect(cells, rownames(protein_data))
list(pos = cells[protein_data[cells, "KLRG1"] >= threshold], neg = cells[protein_data[cells, "KLRG1"] < threshold])
}
run_checked_dge <- function(group_list, F_mat, L_mat, label) {
if (length(group_list$pos) < 3 || length(group_list$neg) < 3) stop(paste("Insufficient data:", label))
df <- FlashierDGE_corrected(F1 = F_mat, L1 = L_mat, group1 = group_list$pos, group2 = group_list$neg)$diff_factors
if (!"SYMBOL" %in% colnames(df)) df$SYMBOL <- rownames(df)
df
}
plot_target_gps <- function(df, x_var, y_var, label_var, target_gps, highlight_color = "darkorange", background_color = "black",
x_limits = c(-0.5, 0.5), y_limits = c(-0.5, 0.5), background_alpha = 0.5,
xlab = "Difference in Mean Loading", ylab = "Difference in Mean Loading", title = "Comparison of Specific GP Loadings") {
highlight_df <- df %>% filter({{ label_var }} %in% target_gps) %>% mutate(.label_display = as.character({{ label_var }}))
ggplot(df, aes(x = {{ x_var }}, y = {{ y_var }})) +
geom_point(color = background_color, alpha = background_alpha) +
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(data = highlight_df, aes(color = {{ label_var }}), size = 2) +
ggrepel::geom_text_repel(seed = 42, data = highlight_df, aes(label = .label_display, color = {{ label_var }}), max.overlaps = Inf, size = 3.5, box.padding = 0.35, point.padding = 0.5, segment.color = "grey50", show.legend = FALSE) +
scale_color_manual(values = highlight_color, guide = "none") +
coord_cartesian(xlim = x_limits, ylim = y_limits) +
labs(x = xlab, y = ylab, title = title) +
theme_minimal()
}
klrg1_threshold <- threshold_results_subset_manual$Threshold[threshold_results_subset_manual$Protein == "KLRG1"]
cd8_split <- get_klrg1_split("CD8", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)
CD4_split <- get_klrg1_split("CD4", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)
treg_split <- get_klrg1_split("Treg", seurat_meta_filtered, protein_mat_normalized_lognorm, klrg1_threshold)
diff_CD8 <- run_checked_dge(cd8_split, F_pm_filtered, L_pm_filtered, "CD8") %>% rename(mean_change_CD8 = mean_change_loadings, AveExpr_CD8 = AveExpr)
diff_CD4 <- run_checked_dge(CD4_split, F_pm_filtered, L_pm_filtered, "CD4") %>% rename(mean_change_CD4 = mean_change_loadings, AveExpr_CD4 = AveExpr)
diff_Treg <- run_checked_dge(treg_split, F_pm_filtered, L_pm_filtered, "Treg") %>% rename(mean_change_Treg = mean_change_loadings, AveExpr_Treg = AveExpr)
# 6g: CD8 vs CD4
merged_cd4 <- inner_join(diff_CD4, diff_CD8, by = "SYMBOL")
p_6g <- plot_target_gps(
df = merged_cd4, x_var = mean_change_CD8, y_var = mean_change_CD4, label_var = SYMBOL,
target_gps = c("GP10", "GP58", "GP25", "GP26", "GP43"), background_alpha = 0.8, x_limits = c(-0.2, 0.4), y_limits = c(-0.2, 0.4),
highlight_color = c("GP10" = "darkorange2", "GP25" = "blue", "GP43" = "blue", "GP26" = "blue", "GP58" = "darkorange2"),
title = "KLRG1 Modulation: CD8 vs CD4", xlab = "Effect Size in CD8 (KLRG1+ - KLRG1-)", ylab = "Effect Size in CD4 (KLRG1+ - KLRG1-)"
) + theme_bw()
ggsave(paste0(figure_path, "6g.pdf"), p_6g, width = 7, height = 6)
# 6h: CD8 vs Treg
merged_treg <- inner_join(diff_Treg, diff_CD8, by = "SYMBOL")
p_6h <- plot_target_gps(
df = merged_treg, x_var = mean_change_CD8, y_var = mean_change_Treg, label_var = SYMBOL,
target_gps = c("GP6", "GP10", "GP12", "GP27", "GP68", "GP58"), background_alpha = 0.8, x_limits = c(-0.2, 0.4), y_limits = c(-0.2, 0.4),
highlight_color = c("GP10" = "darkorange2", "GP27" = "deeppink", "GP6" = "deeppink", "GP68" = "deeppink", "GP12" = "deeppink", "GP58" = "darkorange2"),
title = "KLRG1 Modulation: CD8 vs Treg", xlab = "Effect Size in CD8 (KLRG1+ - KLRG1-)", ylab = "Effect Size in Treg (KLRG1+ - KLRG1-)"
) + theme_bw()
ggsave(paste0(figure_path, "6h.pdf"), p_6h, width = 7, height = 6)
Fig. 6g, h. KLRG1 modulation of GPs across lineages. Within each lineage, cells are split KLRG1+ versus KLRG1- based on the threshold on the KLRG1 protein, and for every GP the effect size is the difference in mean GP loading (KLRG1+ minus KLRG1-). The scatter compares CD8 (x-axis) against CD4 (g) or Treg (h) (y-axis); each point is a GP, the dashed red line marks equal effect (y = x), and the dashed blue lines mark zero. Selected GPs are labeled – orange for large, concordant effects in both lineages, and blue/pink for lineage-biased GPs.
(i-k) GPs associated with CD69
# ============================================================
# 6i/6j/6k: the 10 GPs most associated with CD69
# ============================================================
D_scale6 <- diag(1 / apply(F_pm_filtered, 2, function(x) max(abs(x), na.rm = TRUE)))
F_pm_filtered_scaled <- F_pm_filtered %*% D_scale6
colnames(F_pm_filtered_scaled) <- paste0("GP", 1:ncol(F_pm_filtered_scaled))
cd69_top_gps_subset <- c("GP35", "GP6", "GP170", "GP26", "GP58", "GP171", "GP63", "GP62", "GP3", "GP29")
shared_cells_cd69 <- intersect(rownames(L_pm_filtered), rownames(protein_mat_normalized_lognorm))
cd69_expr_vec <- protein_mat_normalized_lognorm[shared_cells_cd69, "CD69"]
cd69_corr <- sapply(cd69_top_gps_subset, function(gp) cor(L_pm_filtered[shared_cells_cd69, gp], cd69_expr_vec, method = "spearman"))
cd69_top_gps_sorted <- names(sort(cd69_corr, decreasing = FALSE)) # most-correlated GP ends up at top of y-axis
plot_factor_heatmap <- function(F_matrix, gp_vector, n_top = 5, min_abs_loading = 0.5, transpose = FALSE,
title = "Factor loadings – top genes per GP", low_color = "steelblue", mid_color = "white", high_color = "firebrick", font_size = 9) {
F_sub <- F_matrix[, gp_vector, drop = FALSE]
selected_genes <- lapply(gp_vector, function(gp) {
vals <- F_sub[, gp]
top_pos <- names(sort(vals, decreasing = TRUE))[seq_len(min(n_top, sum(vals > 0)))]
top_neg <- names(sort(vals, decreasing = FALSE))[seq_len(min(n_top, sum(vals < 0)))]
c(top_pos, top_neg)
})
selected_genes <- unique(unlist(selected_genes))
if (min_abs_loading > 0) {
max_abs <- apply(F_sub[selected_genes, , drop = FALSE], 1, function(x) max(abs(x), na.rm = TRUE))
selected_genes <- names(max_abs[max_abs >= min_abs_loading])
}
hc_genes <- hclust(dist(F_sub[selected_genes, , drop = FALSE]))
gene_order <- rownames(F_sub[selected_genes, , drop = FALSE])[hc_genes$order]
plot_df <- F_sub[selected_genes, , drop = FALSE] %>%
as.data.frame() %>%
tibble::rownames_to_column("Gene") %>%
tidyr::pivot_longer(cols = -Gene, names_to = "GP", values_to = "Loading") %>%
mutate(GP = factor(GP, levels = gp_vector), Gene = factor(Gene, levels = gene_order))
limit <- max(abs(plot_df$Loading), na.rm = TRUE)
x_aes <- if (transpose) "Gene" else "GP"
y_aes <- if (transpose) "GP" else "Gene"
ggplot(plot_df, aes(x = .data[[x_aes]], y = .data[[y_aes]], fill = Loading)) +
geom_tile() +
scale_fill_gradient2(low = low_color, mid = mid_color, high = high_color, midpoint = 0, limits = c(-limit, limit), name = "Loading") +
# Axis titles are the faceting variables themselves ("Gene" / "GP"), as in
# the published 6i -- an earlier version dropped them along with the title's
# "- top genes per GP" suffix.
labs(title = title, x = x_aes, y = y_aes) +
theme_minimal(base_size = font_size) +
theme(axis.text.x = element_text(angle = if (transpose) 90 else 45, hjust = 1, size = font_size), axis.text.y = element_text(size = font_size), panel.grid = element_blank(), plot.title = element_text(face = "bold"))
}
p_heatmap <- plot_factor_heatmap(F_matrix = F_pm_filtered_scaled, gp_vector = cd69_top_gps_sorted, n_top = 5, font_size = 9, transpose = TRUE, low_color = "#4DAF4A", mid_color = "white", high_color = "#984EA3")
corr_strip_df <- data.frame(GP = factor(cd69_top_gps_sorted, levels = cd69_top_gps_sorted), Correlation = cd69_corr[cd69_top_gps_sorted], x = "Corr")
corr_limit <- max(abs(corr_strip_df$Correlation))
p_corr_strip <- ggplot(corr_strip_df, aes(x = x, y = GP, fill = Correlation)) +
geom_tile() +
scale_fill_gradient2(low = "royalblue", mid = "white", high = "tomato", midpoint = 0, limits = c(-corr_limit, corr_limit), name = "Corr\n(CD69)") +
labs(x = NULL, y = NULL) +
theme_minimal(base_size = 9) +
theme(axis.text.x = element_text(size = 9, angle = 45, hjust = 1), axis.text.y = element_blank(), axis.ticks.y = element_blank(), panel.grid = element_blank())
p_6i <- p_corr_strip + p_heatmap + patchwork::plot_layout(widths = c(0.06, 1), guides = "collect")
ggsave(paste0(figure_path, "6i.pdf"), p_6i, width = 11, height = 5)
# 6j/6k: mean loading of these GPs per tissue (j) and per lineage (k)
cells_for_heatmap <- intersect(rownames(L_pm_filtered), rownames(seurat_meta_filtered))
L_cd69_sub <- L_pm_filtered[cells_for_heatmap, cd69_top_gps_sorted, drop = FALSE]
meta_hm <- seurat_meta_filtered[cells_for_heatmap, c("annotation_level1", "organ_simplified")]
mean_loading_long <- function(L_mat, group_vec, gp_levels) {
as.data.frame(L_mat) %>%
mutate(group = group_vec) %>%
tidyr::pivot_longer(cols = -group, names_to = "GP", values_to = "Loading") %>%
group_by(group, GP) %>%
summarise(mean_loading = mean(Loading, na.rm = TRUE), .groups = "drop") %>%
mutate(GP = factor(GP, levels = gp_levels))
}
make_mean_loading_heatmap <- function(df, title) {
fill_max <- max(df$mean_loading, na.rm = TRUE)
ggplot(df, aes(x = group, y = GP, fill = mean_loading)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "firebrick", limits = c(0, fill_max), name = "Mean\nloading") +
labs(title = title, x = NULL, y = NULL) +
theme_minimal(base_size = 9) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9), axis.text.y = element_text(size = 9), panel.grid = element_blank())
}
df_organ <- mean_loading_long(L_cd69_sub, meta_hm$organ_simplified, cd69_top_gps_sorted)
p_6j <- make_mean_loading_heatmap(df_organ, "Mean GP loading by tissue (organ_simplified)")
ggsave(paste0(figure_path, "6j.pdf"), p_6j, width = 9, height = 5)
df_level1 <- mean_loading_long(L_cd69_sub, meta_hm$annotation_level1, cd69_top_gps_sorted)
p_6k <- make_mean_loading_heatmap(df_level1, "Mean GP loading by cell type (level1)")
ggsave(paste0(figure_path, "6k.pdf"), p_6k, width = 7, height = 5)
Fig. 6i-k. GPs associated with CD69. The ten GPs most associated with CD69 are ordered by the Spearman correlation between CD69 protein expression and GP loading. (i) Heatmap of the top five gene scores per GP (green-white-purple), with a left strip giving each GP’s CD69 correlation (blue-white-red). (j) Mean loading of these GPs per tissue and (k) per lineage.
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