Figure 3: GPs and lineages
Ziang Zhang
Last updated: 2026-07-27
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immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 | 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/Figure3.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 3. GPs and lineages.
#
# Panels produced. NOTE the renumbering: this figure's published counterpart is
# figures/Previous/bits/Figure *2* (2A-2M), letter for letter. Full caption
# text: figures/Previous/bits/Figure 2/Figure2_caption.md.
# 3A Swarm plot of per-GP AUC (predicting major lineage), up-regulated
# GPs only, with GP3/22/29 force-highlighted.
# 3B Structure plot of 6 lineage-defining GPs across major lineages.
# 3C,3E,3G GP loading on the global MDE, for GP68, GP30, GP58.
# 3D,3F,3H Per-gene view (score vs. mean expression) for GP68, GP30, GP58.
# 3I MDE restricted to gdT/CD8aa/DN, colored by lineage.
# 3J,3K,3L GP loading on the gdT/CD8aa/DN MDE, for GP22, GP29, GP3.
# 3M Heatmap of top-30 gene scores for GP3, GP22, GP29.
#
# Source: ported from Figure_Lineage.R, which also produced the
# Figure S2 panels (see FigureS2.R) from the same L_pm_filtered/MDE setup.
#
# Panels 3C/3E/3G (GP68/30/58 on the *global* MDE) have no direct
# equivalent in the original script -- Figure_Lineage.R only plots
# GP68/30/58-defined structure/boxplots, never their global-MDE loading.
# Reconstructed here with the same plot_loadings_on_mde() styling used for
# panels 3J/3K/3L (the closest in-script precedent).
#
# Panels 3D/3F/3H and 3M are NOT from the original pre-refactor analysis
# code at all -- they're generated by the separate "immgen-signature"
# Shiny app (see code/R/volcano_helpers.R and code/R/cross_gp_helpers.R
# headers for the full provenance notes).
#
# 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, F_pm_filtered.rds [code/pipeline/01b_filter_cells.R]
# umap_result.rds [gap, no producer script here]
# level_1_AUC_list_figure_no_thymocytes_healthy.rds [code/pipeline/02_compute_auc.R]
# mean_shifted_log_expr.rds [gap, no producer script here]
library(ggplot2)
library(ggrepel)
library(dplyr)
library(tidyr)
library(tibble)
library(scattermore)
library(pheatmap)
library(fastTopics) # structure_plot()
data_path <- "data/"
figure_path <- "figures/final-selected/Figure 3/"
source("code/R/plot_utils.R") # lineage_colors()
source("code/R/lineage_plots.R") # plot_gp_swarm(), plot_loadings_on_mde()
source("code/R/volcano_helpers.R") # plot_gp_signature_volcano(), normalize_maxabs()
source("code/R/cross_gp_helpers.R") # plot_cross_gp_heatmap()
# ============================================================
# 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"))
seurat_meta_filtered <- seurat_meta[rownames(L_pm_filtered), ]
mde_result <- readRDS(paste0(data_path, "umap_result.rds"))
colnames(mde_result) <- c("MDE_1", "MDE_2")
mde_result <- mde_result[rownames(L_pm_filtered), ]
df_mde <- as.data.frame(mde_result)
level_1_AUC_list <- readRDS(paste0(data_path, "level_1_AUC_list_figure_no_thymocytes_healthy.rds"))
# Rename K## to GP## for display consistency
colnames(L_pm_filtered) <- gsub("^K", "GP", colnames(L_pm_filtered))
colnames(level_1_AUC_list$auc) <- gsub(
"^K",
"GP",
colnames(level_1_AUC_list$auc)
)(A) AUC swarm plot by lineage
# ============================================================
# 3A: AUC swarm, up-regulated GPs only, GP3/22/29 forced highlight
# ============================================================
selected_lineage_in_order <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN")
level_1_categories <- as.character(unique(
seurat_meta_filtered$annotation_level1
))
mean_loading_matrix <- t(sapply(level_1_categories, function(cat) {
cells_in_cat <- which(seurat_meta_filtered$annotation_level1 == cat)
colMeans(L_pm_filtered[cells_in_cat, , drop = FALSE], na.rm = TRUE)
}))
mean_loading_matrix_selected <- mean_loading_matrix[selected_lineage_in_order, ]
non_thymocyte_cells <- which(
seurat_meta_filtered$annotation_level1 != "thymocyte"
)
mean_loading_vec <- colMeans(
L_pm_filtered[non_thymocyte_cells, , drop = FALSE],
na.rm = TRUE
)
AUC_mat_selected <- level_1_AUC_list$auc[selected_lineage_in_order, ]
p_3A <- plot_gp_swarm(
AUC_mat_selected,
loading_mat = mean_loading_matrix,
overall_loading_vec = mean_loading_vec,
filter_positive = TRUE,
forced_highlights = list(
gdT = c("GP22", "GP29", "GP3"),
CD8aa = c("GP22", "GP29", "GP3"),
DN = c("GP22", "GP29", "GP3")
),
top_k_labels = 1,
threshold_line = 0.5,
title = "Up-regulated GP Predictive Performance (AUC)",
subtitle = "Up-regulated GPs (Loading >= Overall Mean); triangles mark forced highlights that are down-regulated",
y_label = "Area Under the Curve (AUC)",
italic_subtitle = TRUE
)
ggsave(
filename = paste0(figure_path, "3A.pdf"),
plot = p_3A,
width = 10,
height = 6
)
Fig. 3A. Swarm plot of per-GP predictive performance (one-vs-rest AUC, computed on healthy non-thymocyte cells) across the seven major lineages (CD8, CD4, Treg, gdT, CD8aa, Tz, DN). Only up-regulated GPs (mean loading >= the overall mean) are plotted as circles; triangles mark forced-highlight GPs (GP3, GP22, GP29) that are down-regulated in gdT, CD8aa, and DN; the dashed line marks AUC = 0.5, and the most predictive GP per lineage is labeled.
(B) Structure plot of lineage-defining GPs
# ============================================================
# 3B: Structure plot of 6 lineage-defining GPs
# ============================================================
set.seed(1234)
color_coding <- ZemmourLib::immgent_colors$level1
level1_category_to_factor <- c(
"Treg" = "GP68",
"CD8" = "GP58",
"Tz" = "GP30",
"DN" = "GP22",
"CD8aa" = "GP29",
"gdT" = "GP3"
)
fit2 <- L_pm_filtered[, level1_category_to_factor, drop = FALSE]
cell_type <- seurat_meta[rownames(fit2), "annotation_level1"]
cells <- which(cell_type == "CD4" | cell_type == "CD8")
cells <- sample(cells, 1e5)
cells <- sort(c(
cells,
which(
cell_type != "CD4" &
cell_type != "CD8" &
cell_type != "thymocyte" &
cell_type != "DP"
)
))
p_3B <- structure_plot(
fit2[cells, ],
gap = 40,
n = 10000,
colors = color_coding[names(level1_category_to_factor)],
grouping = cell_type[cells]
) +
labs(y = "membership", color = "", fill = "") +
guides(fill = guide_legend(nrow = 1), color = guide_legend(nrow = 1)) +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "horizontal",
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
legend.text = element_text(size = 12),
legend.title = element_text(size = 13)
)
ggsave(
filename = paste0(figure_path, "3B.pdf"),
plot = p_3B,
width = 10,
height = 6,
dpi = 300
)
Fig. 3B. Structure plot of the membership (GP loading) of six lineage-defining GPs (GP58, GP68, GP30, GP3, GP29, GP22) across cells grouped by major lineages (CD8, CD4, Treg, gdT, CD8aa, Tz, DN); CD4 and CD8 cells are subsampled for visualization.
(C) GP68 loading on the global MDE
# ============================================================
# 3C/3E/3G: GP68, GP30, GP58 loading on the global MDE (reconstructed)
# ============================================================
p_3C <- plot_loadings_on_mde(
mde = df_mde,
loading = L_pm_filtered[rownames(df_mde), "GP68"],
factor_num = 68,
size = 0.6,
bg_alpha = 0.1,
bg_color = "grey90"
)
ggsave(
filename = paste0(figure_path, "3C.pdf"),
plot = p_3C,
width = 5,
height = 4
)
Fig. 3C. GP68 loading projected onto the global MDE embedding; each point is a cell, colored by its loading for GP68.
(D) GP68 signature volcano
# ============================================================
# 3D/3F/3H: "signature volcano" per-gene view for GP68, GP30, GP58.
# NOT from the original pre-refactor analysis code -- generated by the
# separate "immgen-signature" Shiny app (see code/R/volcano_helpers.R header). n_label was
# bisected per-GP against the published PDF's byte size (apparently tuned
# per-panel interactively in the app); GP68 matches exactly, GP30/GP58
# within ~10 bytes.
# ============================================================
F_pm_filtered_3d <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
colnames(F_pm_filtered_3d) <- paste0("GP", seq_len(ncol(F_pm_filtered_3d)))
F_pm_normalized_3d <- normalize_maxabs(F_pm_filtered_3d)
mean_shifted_log_expr <- readRDS(paste0(data_path, "mean_shifted_log_expr.rds"))
p_3D <- plot_gp_signature_volcano(
"GP68",
F_pm_normalized_3d,
mean_shifted_log_expr,
threshold = 0.1,
n_label = 100,
bg_alpha = 0.2
)
ggsave(
filename = paste0(figure_path, "3D.pdf"),
plot = p_3D,
width = 7,
height = 5.5
)
Fig. 3D. Per-gene view of GP68: each gene’s score in the GP (x-axis, scaled so the maximum |score| = 1) versus its mean shifted-log expression (y-axis); top genes are labeled.
(E) GP30 loading on the global MDE
p_3E <- plot_loadings_on_mde(
mde = df_mde,
loading = L_pm_filtered[rownames(df_mde), "GP30"],
factor_num = 30,
size = 0.6,
bg_alpha = 0.1,
bg_color = "grey90"
)
ggsave(
filename = paste0(figure_path, "3E.pdf"),
plot = p_3E,
width = 5,
height = 4
)
Fig. 3E. GP30 loading projected onto the global MDE embedding; each point is a cell, colored by its loading for GP30.
(F) GP30 signature volcano
p_3F <- plot_gp_signature_volcano(
"GP30",
F_pm_normalized_3d,
mean_shifted_log_expr,
threshold = 0.1,
n_label = 100,
bg_alpha = 0.2
)
ggsave(
filename = paste0(figure_path, "3F.pdf"),
plot = p_3F,
width = 7,
height = 5.5
)
Fig. 3F. Per-gene view of GP30: each gene’s score in the GP (x-axis, scaled so the maximum |score| = 1) versus its mean shifted-log expression (y-axis); top genes are labeled.
(G) GP58 loading on the global MDE
p_3G <- plot_loadings_on_mde(
mde = df_mde,
loading = L_pm_filtered[rownames(df_mde), "GP58"],
factor_num = 58,
size = 0.6,
bg_alpha = 0.1,
bg_color = "grey90"
)
ggsave(
filename = paste0(figure_path, "3G.pdf"),
plot = p_3G,
width = 5,
height = 4
)
Fig. 3G. GP58 loading projected onto the global MDE embedding; each point is a cell, colored by its loading for GP58.
(H) GP58 signature volcano
p_3H <- plot_gp_signature_volcano(
"GP58",
F_pm_normalized_3d,
mean_shifted_log_expr,
threshold = 0.1,
n_label = 83,
bg_alpha = 0.2
)
ggsave(
filename = paste0(figure_path, "3H.pdf"),
plot = p_3H,
width = 7,
height = 5.5
)
Fig. 3H. Per-gene view of GP58: each gene’s score in the GP (x-axis, scaled so the maximum |score| = 1) versus its mean shifted-log expression (y-axis); top genes are labeled.
(I) gdT/CD8aa/DN MDE by lineage
# ============================================================
# 3I: MDE restricted to gdT/CD8aa/DN, colored by lineage
# (excludes proliferating / miniverse subsets)
# ============================================================
gcd_lineages <- c("gdT", "CD8aa", "DN")
gcd_cells <- seurat_meta_filtered$cellID[
seurat_meta_filtered$annotation_level1 %in%
gcd_lineages &
!(seurat_meta_filtered$annotation_level2_group %in%
c("proliferating", "miniverse"))
]
df_mde_gcd <- df_mde[gcd_cells, ]
df_mde_gcd$annotation_level1 <- factor(
seurat_meta_filtered[gcd_cells, "annotation_level1"],
levels = gcd_lineages
)
p_3I <- ggplot(df_mde_gcd, aes(x = MDE_1, y = MDE_2)) +
scattermore::geom_scattermore(
aes(color = annotation_level1),
pointsize = 1.2
) +
scale_color_manual(values = lineage_colors()[gcd_lineages]) +
coord_equal() +
theme_classic() +
labs(
title = "MDE: gdT / CD8aa / DN",
x = "MDE 1",
y = "MDE 2",
color = "Cell Type"
) +
theme(
legend.text = element_text(size = 10),
legend.key.size = unit(1.5, "lines")
) +
guides(color = guide_legend(override.aes = list(size = 4)))
ggsave(
filename = paste0(figure_path, "3I.pdf"),
plot = p_3I,
width = 5,
height = 5
)
Fig. 3I. MDE restricted to gdT, CD8aa, and DN cells (excluding proliferating and miniverse subsets), colored by lineage.
(J-L) GP22, GP29, GP3 loading on the gdT/CD8aa/DN MDE
# ============================================================
# 3J/3K/3L: GP22, GP29, GP3 loading on the gdT/CD8aa/DN MDE
# ============================================================
gp_letter <- c("GP22" = "3J", "GP29" = "3K", "GP3" = "3L")
for (gp_name in names(gp_letter)) {
gp_num <- as.numeric(sub("^GP", "", gp_name))
p_loading <- plot_loadings_on_mde(
mde = df_mde_gcd[, c("MDE_1", "MDE_2")],
loading = L_pm_filtered[gcd_cells, gp_name],
factor_num = gp_num,
size = 0.6,
bg_alpha = 0.1,
bg_color = "grey90"
)
ggsave(
filename = paste0(figure_path, gp_letter[gp_name], ".pdf"),
plot = p_loading,
width = 5,
height = 4
)
}
Fig. 3J-L. GP loading on the same gdT/CD8aa/DN MDE for GP22 (J), GP29 (K), and GP3 (L).
(M) Top-30 gene score heatmap for GP3/GP29/GP22
# ============================================================
# 3M: cross-GP heatmap, top-30 gene weights for GP3, GP29, GP22
# (immgen-signature app's mod_cross_gp.R heatmap mode; see
# code/R/cross_gp_helpers.R)
#
# Column order (GP3, GP29, GP22) and pinning Fcer1g/Ccl5/Cd7 to the top 3
# rows are our own editorial choices, made after the app-faithful version
# (GP3, GP22, GP29; rows purely hclust-ordered) -- see plot_cross_gp_heatmap()'s
# `pin_top` argument. This means 3M is no longer a byte-exact reproduction
# of the published PDF (see script/README.md).
# ============================================================
F_pm_filtered_3m <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
colnames(F_pm_filtered_3m) <- paste0("GP", seq_len(ncol(F_pm_filtered_3m)))
F_pm_norm_3m <- normalize_maxabs(F_pm_filtered_3m)
common_genes_3m <- intersect(rownames(F_pm_norm_3m), names(mean_shifted_log_expr))
gps_3m <- c("GP3", "GP29", "GP22")
mat_3m <- F_pm_norm_3m[common_genes_3m, gps_3m, drop = FALSE]
p_3M <- plot_cross_gp_heatmap(
mat_3m, gps_3m,
# direction = "pos" ("Positive only" in the app's sidebar, not its "both"
# default) is what the published panel was exported with: under "both" the
# top-30 by max|score| pulls in 11 all-negative housekeeping genes
# (Tpt1/Actb/Eef1a1/...) and pushes out Ikzf2/Klrd1/Il2rb/Itgae/Dapk2/Junb/
# Cd3g/Ly6e/Ppia/Malat1/Mir6236. Verified: "pos" reproduces the published
# 30-gene set exactly.
feat_label = "Gene", n_genes = 30, direction = "pos",
threshold = 0.05, colorscheme = "bwr", cluster_r = TRUE, cluster_c = FALSE,
pin_top = c("Fcer1g", "Ccl5", "Cd7")
)
ph_3m <- max(4, 2 + 30 * 0.14)
pw_3m <- max(5, 3 + length(gps_3m) * 0.8)
ggsave(filename = paste0(figure_path, "3M.pdf"), plot = p_3M, width = pw_3m, height = ph_3m)
Fig. 3M. Heatmap of the top 30 gene scores across GP3, GP29, and GP22 (blue, negative; red, positive), with Fcer1g, Ccl5, and Cd7 pinned to the top rows.
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