Extended Data Figure 2: Tz- and CD8-specific GPs (GP30 and GP58, respectively), and GP activity across T cell clusters
Ziang Zhang
Last updated: 2026-09-10
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Knit directory:
immgenT-GP-analysis/analysis/
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past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c21f51f | Ziang Zhang | 2026-09-10 | Remove the per-lineage structure-plot figure; keep its record for Figure 4 |
| html | 60125af | Ziang Zhang | 2026-09-10 | Build site: Figure 4b on the shared column colours |
| html | 88ee847 | Ziang Zhang | 2026-09-10 | Build site: Figure 4b without the miniverse clusters |
| html | 5874416 | Ziang Zhang | 2026-09-10 | Build site: main Figure 4 inserted, Extended Data back to 1-7 |
| 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 | 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 |
| Rmd | 66aa029 | Ziang Zhang | 2026-07-30 | Name the Extended Data figures as published on the site |
| 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 | 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 | 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 |
| 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 |
Panels (a)-(c) are produced by script/FigureS2.R,
which shares its source data setup with Figure
3. Panel (d) is produced by script/FigureS2d.R,
which shares nothing with them – a different reference cell set and a
different rendering module – and so is kept as its own script. The code
below is shown for reference (not re-executed on this page); the images
are its pre-rendered output.
Setup
# Figure S2. GP30 and GP58 loadings across T-cell subsets.
#
# Panels produced:
# S2A Boxplots of GP30 loading across Tz subsets (iNKT, MAIT, other Tz)
# vs all other, non-Tz T cells.
# S2B Boxplots of GP58 loading: resting CD8, activated CD8, vs the other
# six T-cell lineages pooled as "Other T cells".
# S2C Boxplots of CD8A/CD8B log-normalized CITE-seq protein expression in
# CD8 cells, resting vs activated.
#
# Required inputs (data/) -- see code/README.md's "Data provenance" table
# for the full picture:
# igt1_96_..._ADTonly.Rds [primary input Seurat object]
# L_pm_filtered.rds [code/pipeline/01b_filter_cells.R]
# protein_mat_normalized_lognorm.rds [code/other/prepare_citeseq_protein_matrices_20260206.R]
library(ggplot2)
library(dplyr)
library(ggrastr)
library(tidyr)
library(Matrix) # protein_mat_normalized_lognorm is a dgCMatrix
data_path <- "data/"
figure_path <- "figures/final-selected/Figure S2/"
source("code/R/plot_utils.R") # tukey_outliers()
# ============================================================
# Load data
# ============================================================
seurat_meta <- readRDS(paste0(data_path, "igt1_96_withtotalvi20260206_clean_ADTonly.Rds"))@meta.data
L_pm_filtered <- readRDS(paste0(data_path, "L_pm_filtered.rds"))
colnames(L_pm_filtered) <- gsub("^K", "GP", colnames(L_pm_filtered))
seurat_meta_filtered <- seurat_meta[rownames(L_pm_filtered), ]
selected_lineage_in_order <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN")(a) GP30 loading across Tz subsets
# ============================================================
# S2A: GP30 loading, Tz subsets (iNKT/MAIT/other Tz) vs other T cells
# ============================================================
GP30_df <- seurat_meta_filtered %>%
select(cellID, annotation_level1, iNKT, MAIT) %>%
filter(annotation_level1 != "thymocyte") %>%
mutate(GP30_loading = L_pm_filtered[cellID, "GP30"]) %>%
mutate(
Group = case_when(
annotation_level1 == "Tz" & iNKT ~ "iNKT",
annotation_level1 == "Tz" & MAIT ~ "MAIT",
annotation_level1 == "Tz" ~ "Other Tz",
TRUE ~ "Other T cells"
),
Group = factor(Group, levels = c("iNKT", "MAIT", "Other Tz", "Other T cells"))
)
p_S2A <- ggplot(GP30_df, aes(x = Group, y = GP30_loading, fill = Group)) +
geom_boxplot(outlier.shape = NA) +
ggrastr::rasterise(
geom_point(data = tukey_outliers(GP30_df, "GP30_loading", "Group"), size = 0.5, alpha = 0.3, show.legend = FALSE),
dpi = 300
) +
scale_fill_manual(values = c("iNKT" = "darkgoldenrod2", "MAIT" = "darkgoldenrod3", "Other Tz" = "darkgoldenrod1", "Other T cells" = "grey70")) +
labs(title = "GP30 loading across Tz subsets and other T cells", x = NULL, y = "GP30 loading") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", hjust = 0.5), panel.grid.major.x = element_blank(), legend.position = "none")
ggsave(filename = paste0(figure_path, "S2A.pdf"), plot = p_S2A, width = 6, height = 5)
Extended Data Fig. 2a. Box plots showing the distribution of GP30 activity across Tz subsets—iNKT, MAIT, and other Tz cells (that is, Tz cells that are neither iNKT nor MAIT)—and all other T cells.
(b) GP58 loading, resting/activated CD8
# ============================================================
# S2B: GP58 loading, resting/activated CD8 vs other T cells
# ============================================================
non_CD8_lineages <- setdiff(selected_lineage_in_order, "CD8")
GP58_df <- seurat_meta_filtered %>%
select(cellID, annotation_level1, annotation_level2_group) %>%
mutate(GP58_loading = L_pm_filtered[cellID, "GP58"]) %>%
mutate(
Group = case_when(
annotation_level1 == "CD8" & annotation_level2_group == "resting" ~ "CD8 (Resting)",
annotation_level1 == "CD8" & annotation_level2_group == "activated" ~ "CD8 (Activated)",
annotation_level1 %in% non_CD8_lineages ~ "Other T cells",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Group)) %>%
mutate(Group = factor(Group, levels = c("CD8 (Resting)", "CD8 (Activated)", "Other T cells")))
p_S2B <- ggplot(GP58_df, aes(x = Group, y = GP58_loading, fill = Group)) +
geom_boxplot(outlier.shape = NA) +
ggrastr::rasterise(
geom_point(data = tukey_outliers(GP58_df, "GP58_loading", "Group"), size = 0.5, alpha = 0.3, show.legend = FALSE),
dpi = 300
) +
scale_fill_manual(values = c("CD8 (Resting)" = "darkorange2", "CD8 (Activated)" = "orange", "Other T cells" = "grey70")) +
labs(title = "GP58 loading across CD8 subsets and other T cells", x = NULL, y = "GP58 loading") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", hjust = 0.5), panel.grid.major.x = element_blank(), legend.position = "none")
ggsave(filename = paste0(figure_path, "S2B.pdf"), plot = p_S2B, width = 5, height = 5)
Extended Data Fig. 2b. Box plots showing GP58 activity in resting and activated CD8+ T cells compared with all other T cells.
(c) CD8A/CD8B protein expression
# ============================================================
# S2C: CD8A/CD8B CITE-seq protein expression, resting vs activated CD8
# ============================================================
protein_mat_normalized_lognorm <- readRDS(paste0(data_path, "protein_mat_normalized_lognorm.rds"))
CD8_citeseq_cells <- seurat_meta_filtered$cellID[
seurat_meta_filtered$annotation_level1 == "CD8" &
seurat_meta_filtered$cite_seq &
seurat_meta_filtered$annotation_level2_group %in% c("resting", "activated")
]
CD8_citeseq_cells <- intersect(CD8_citeseq_cells, rownames(protein_mat_normalized_lognorm))
CD8_protein_df <- data.frame(
cellID = CD8_citeseq_cells,
Group = ifelse(seurat_meta_filtered[CD8_citeseq_cells, "annotation_level2_group"] == "resting", "CD8 (Resting)", "CD8 (Activated)"),
CD8A = protein_mat_normalized_lognorm[CD8_citeseq_cells, "CD8A"],
CD8B = protein_mat_normalized_lognorm[CD8_citeseq_cells, "CD8B"]
) %>%
tidyr::pivot_longer(cols = c("CD8A", "CD8B"), names_to = "Protein", values_to = "Expression") %>%
mutate(Group = factor(Group, levels = c("CD8 (Resting)", "CD8 (Activated)")))
p_S2C <- ggplot(CD8_protein_df, aes(x = Protein, y = Expression, fill = Group)) +
geom_boxplot(outlier.shape = NA) +
ggrastr::rasterise(
geom_point(
data = tukey_outliers(CD8_protein_df, "Expression", c("Protein", "Group")),
aes(group = Group), size = 0.5, alpha = 0.3, position = position_dodge(width = 0.75), show.legend = FALSE
),
dpi = 300
) +
scale_fill_manual(values = c("CD8 (Resting)" = "darkorange2", "CD8 (Activated)" = "orange")) +
labs(title = "CD8A / CD8B protein expression in CD8 cells", x = NULL, y = "Log-normalized protein expression", fill = NULL) +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", hjust = 0.5), panel.grid.major.x = element_blank(), legend.position = "top")
ggsave(filename = paste0(figure_path, "S2C.pdf"), plot = p_S2C, width = 5, height = 5)
Extended Data Fig. 2c. Box plots of CD8A and CD8B protein expression (CITE-seq, log-normalized counts) in resting and activated CD8+ T cells.
(d) GP activity across T cell clusters
This panel shows the full row-centered matrix: all 200 GPs against
the 99 level-2 clusters observed in healthy non-thymocyte cells
(condition_broad == "healthy" and
annotation_level1 != "thymocyte"), with the miniverse
(.wM) clusters excluded as in Fig. 1d. For each GP, its mean loading
across clusters is subtracted from every cluster mean, so a row says
where a program is more or less active than its own average rather than
how large its loading is. The centered color scale is fixed at -0.2 to
0.2; values outside this range are saturated at the endpoint colors. The
columns carry the same two annotations Fig. 1d gives its cells – lineage
and annotation_level2_group, each with a legend – rather
than a per-cluster color bar, since the column labels already name every
cluster. The columns are also ordered the way Fig. 1d orders its cells –
lineage, then annotation_level2_group as a contiguous
block, then cluster alphabetically within the block, which is what puts
each lineage’s proliferating (.P) cluster at the end of its
lineage rather than mid-alphabet – and the GP rows follow the columns,
in dominant-cluster blocks. It is the mean-loading counterpart of Figure 4, which resolves the same clusters cell
by cell.
Setup, centering, and ordering
# Figure S2, panel d. GP activity across T cell clusters.
#
# One panel (see analysis/FigureS2.Rmd for the caption text):
# S2D Row-centered mean GP activity across the 99 level-2 clusters, all
# 200 GPs, in healthy non-thymocyte cells.
#
# Panels S2A-S2C come from script/FigureS2.R; this panel is kept in its own
# script because it shares nothing with them -- a different reference cell
# set, a different rendering module -- and re-running it means loading the
# full cell-by-GP loading matrix.
#
#
# For each GP, its mean loading across clusters is subtracted from every cluster
# mean, so the panel shows where a program is more or less active than its own
# average rather than how large its loading is. The centered color scale is
# fixed at [-0.2, 0.2]; values outside this range saturate at the endpoint
# colors. Level2 columns follow Figure 1's level1 order, with level2 labels
# alphabetized within each level1 block. The columns are annotated the way
# Figure 1d annotates its cells -- a level1 bar and an annotation_level2_group
# bar, each with a legend, and no per-cluster colour bar, since the column
# labels already name every cluster.
#
# Required inputs (data/) -- see code/README.md's "Data provenance" table
# for the full picture:
# L_pm_filtered.rds [code/pipeline/01b_filter_cells.R]
# igt1_96_..._ADTonly.Rds [primary input Seurat object]
suppressPackageStartupMessages({
library(ComplexHeatmap)
library(circlize)
library(grid)
library(ZemmourLib)
})
if (!file.exists("code/R/setup_data.R")) {
stop("Run this script from the immgenT-GP-analysis repository root.")
}
source("code/R/setup_data.R")
source("code/R/centered_mean_heatmap.R")
source("code/R/level2_group_palette.R")
figure_path <- "figures/final-selected/Figure S2"
dir.create(figure_path, recursive = TRUE, showWarnings = FALSE)
# ============================================================
# Setup, centering, and ordering
# ============================================================
gp_data <- load_gp_data()
reference <- healthy_nonthymocyte_reference(gp_data)
# Drop the miniverse clusters, as Figure 1d does: they are the ".wM" clusters,
# eight of them here (Figure 1d sees seven, because it drops DP as well), so
# 107 level2 clusters become 99. The exclusion is applied to the cells before
# any mean is taken, so the centering is over the columns that remain.
keep <- !reference$meta$annotation_level2_group %in% EXCLUDE_LEVEL2_GROUPS
if (anyNA(keep) || !any(keep)) {
stop("annotation_level2_group is missing for some healthy non-thymocyte cells.")
}
L_reference <- reference$L[keep, , drop = FALSE]
meta_reference <- reference$meta[keep, , drop = FALSE]
centered_color_limit <- 0.2
level1_order <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN", "DP")
level2_result <- mean_loading_by_group(L_reference, meta_reference$annotation_level2)
level2_raw <- level2_result$matrix
level2_centered <- center_by_gp_mean(level2_raw)
level2_group_level1 <- level2_to_level1_map(
meta_reference, colnames(level2_raw), level1_order
)
level2_group_group <- level2_to_group_map(meta_reference, colnames(level2_raw))
# Columns follow Figure 1d exactly: lineage, then annotation_level2_group as a
# contiguous block, then cluster alphabetically -- so each lineage's ".P"
# cluster sits at the end of its lineage rather than mid-alphabet. GP rows then
# follow the columns, in dominant-cluster blocks.
level2_order <- dominant_group_order(
level2_raw,
level2_group_block_order(
colnames(level2_raw), level2_group_level1, level2_group_group, level1_order
)
)
stopifnot(
nrow(level2_centered) == 200L,
ncol(level2_centered) == 99L,
!any(level2_group_group %in% EXCLUDE_LEVEL2_GROUPS),
max(abs(rowMeans(level2_centered))) < 1e-12
)
level1_palette <- ZemmourLib::immgent_colors$level1[level1_order]Cluster mean loading
render_centered_heatmap(
level2_centered,
NULL,
"cluster (annotation_level2)",
file.path(figure_path, "S2D.pdf"),
level2_order$row_order,
level2_order$column_order,
centered_color_limit,
paste0(
"all 200 GPs; level2 columns: Figure 1 level1 order ",
"(CD8, CD4, Treg, gdT, CD8aa, Tz, DN, DP); ",
"level2_group blocks, alphabetical within block; ",
"miniverse (.wM) clusters excluded; ",
"GP rows: dominant-group blocks"
),
group_level1 = level2_group_level1,
level1_palette = level1_palette,
group_annotation = level2_group_group,
group_annotation_palette = LEVEL2_GROUP_COLORS[LEVEL2_GROUP_ORDER]
)
summary_dir <- "output/FigureS2d" # build intermediate (not a manuscript panel)
dir.create(summary_dir, recursive = TRUE, showWarnings = FALSE)
write.csv(
data.frame(
panel = "S2D",
grouping = "annotation_level2",
view = "full row-centered mean loading",
gp_count = nrow(level2_centered),
group_count = ncol(level2_centered),
excluded_level2_groups = paste(EXCLUDE_LEVEL2_GROUPS, collapse = ";"),
centered_definition = "group mean minus mean across groups for each GP",
color_min = -centered_color_limit,
color_mid = 0,
color_max = centered_color_limit,
observed_min = min(level2_centered),
observed_max = max(level2_centered)
),
file.path(summary_dir, "S2D_summary.csv"),
row.names = FALSE,
quote = FALSE
)
message("Wrote Figure S2 panel d to ", normalizePath(figure_path))
| Version | Author | Date |
|---|---|---|
| 5874416 | Ziang Zhang | 2026-09-10 |
Extended Data Fig. 2d. Row-centered mean GP activity across the 99 level-2 clusters. Mean GP activity computed in samples at baseline.
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