Extended Data Figure 2: Tz- and CD8-specific GPs (GP30 and GP58, respectively)
Ziang Zhang
Last updated: 2026-09-09
Checks: 7 0
Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 |
All panels are produced by script/FigureS2.R,
which shares its source data setup with Figure
3. 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.
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