Figure S2: GP30 and GP58 loadings across T-cell subsets
Ziang Zhang
Last updated: 2026-07-02
Checks: 5 1
Knit directory:
immgenT-GP-analysis/analysis/
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All panels are produced by script-refactor/FigureS2.R,
which shares its source data setup with Figure
2. 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 (see figures/final-selected/bits/Figure S2/FigureS2_caption.md
# for the full caption text):
# 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.
#
# Source: ported from script/Figure_Lineage.R (see Figure2.R for the main
# Figure 2 panels from the same file).
library(ggplot2)
library(dplyr)
library(ggrastr)
library(tidyr)
library(Matrix) # protein_mat_normalized_lognorm is a dgCMatrix; rownames()
# dispatch on it is unreliable unless Matrix is attached
# (not just loaded as a namespace), which silently intersected
# to zero cells during this refactor -- keep this library() call.
data_path <- "data/"
figure_path <- "figure-refactor/Figure S2/"
source("code-refactor/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)
Fig. S2A. Boxplots of GP30 loading across Tz subsets – iNKT, MAIT, and other Tz (Tz cells that are neither iNKT nor MAIT) – and all other, non-Tz T cells; thymocytes are excluded. Boxes show the median and interquartile range, whiskers extend to 1.5x the interquartile range, and points mark values beyond the whiskers.
(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)
Fig. S2B. Boxplots of GP58 loading in resting CD8, activated CD8 (split by activation annotation), and the other six T-cell lineages pooled as “Other T cells” (CD4, Treg, gdT, CD8aa, Tz, and DN; thymocytes and DP cells are excluded); boxes and outlier points as in (A).
(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)
Fig. S2C. Boxplots of CD8A and CD8B log-normalized surface-protein (CITE-seq) expression in CD8 cells, comparing resting versus activated, restricted to CD8 cells with CITE-seq protein measurements; boxes and outlier points 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: Asia/Tokyo
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 stringr_1.6.0
[25] compiler_4.5.1 fs_1.6.6 Rcpp_1.1.1-1.1 pkgconfig_2.0.3
[29] later_1.4.4 digest_0.6.39 R6_2.6.1 pillar_1.11.1
[33] magrittr_2.0.5 bslib_0.9.0 tools_4.5.1 cachem_1.1.0