Figure S1: GP reproducibility across IGTs
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 |
|---|---|---|---|---|
| Rmd | bf7afcf | Ziang Zhang | 2026-07-27 | Make S1D consistent with S1C, and drop the legacy pages from docs/ |
| html | adaef21 | Ziang Zhang | 2026-07-27 | Build site: panel fixes and PDF-derived assets |
| Rmd | 9e032a7 | Ziang Zhang | 2026-07-27 | Fix four panels that diverged from the published figures; make renders reproducible |
| 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 | c655a83 | Ziang Zhang | 2026-07-16 | Fig S1E: use empirical active-gene/cell counts (match Figure 2) |
| html | c655a83 | Ziang Zhang | 2026-07-16 | Fig S1E: use empirical active-gene/cell counts (match Figure 2) |
| Rmd | c2b3360 | Ziang Zhang | 2026-07-13 | Use log-scale axes for Fig 1E/1F and add Fig S1E gene-vs-cell sparsity scatter |
| html | c2b3360 | Ziang Zhang | 2026-07-13 | Use log-scale axes for Fig 1E/1F and add Fig S1E gene-vs-cell sparsity scatter |
| 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/FigureS1.R.
The code below is shown for reference (not re-executed on this page);
the images are its pre-rendered output. Panels A/B reuse a cached
per-IGT cosine-similarity score matrix
(data/igt_specific_cosine_scores.csv); see
code/pipeline/05_igt_validation.R for how that matrix
itself is produced (a much heavier, cluster-scale computation).
Setup
# Figure S1. GP reproducibility across IGTs.
#
# Panels produced (see figures/Previous/bits/Figure S1/FigureS1_caption.md
# for the full caption text):
# S1A Cumulative number of GPs validated (cosine >= threshold, thresholds
# 0.2-0.8) as IGTs are added one at a time, in IGT index order.
# S1B Number of GPs validated by at least X IGTs, vs X (log-log), for the
# same thresholds.
# S1C Between-IGT variability: each GP's mean-of-per-IGT-mean-loading (x)
# vs. variance-of-per-IGT-mean-loading (y), spleen-standard subset.
# S1D Heatmap of per-IGT mean loading (IGTs with >= 500 spleen-standard
# cells only) for the 10 highest-variance GPs from S1C -- i.e. ranked on
# S1C's own variance, so the two panels agree. Figure_batch.R ranked
# S1D on the >= 500-cell subset while labelling S1C on all 35 IGTs,
# which made the published S1D show GP2 where S1C labels GP25; we fix
# that inconsistency rather than reproduce it.
# S1E Scatter of the NUMBER of active genes (x) vs. proportion of active
# cells (y) per GP, using the same hard-threshold definitions as Figure 2
# (|normalized score| > 0.25 for genes; normalized loading > 0.1 for
# cells), over non-thymocyte cells -- not the EBMF sparsity prior. One
# dot per GP.
#
# Source: S1A/S1B ported from Figure_Saturation.R; S1C/S1D from
# Figure_batch.R (panels a/b only -- that script's `plot_gp_loading()`
# helper is defined but never called for a saved output, so it's dropped).
#
# S1A/S1B reuse the per-IGT cosine-matching score matrix
# (data/igt_specific_cosine_scores.csv) rather than recomputing it here --
# recomputing requires Hungarian-matching each of the ~80 per-IGT
# refactorizations in data/igt_specific/*.qs against the full model, which is
# the job of code/pipeline/05_igt_validation.R (run once upstream).
#
# Required inputs (data/) -- see code/README.md's "Data provenance" table
# for the full picture:
# igt_specific_cosine_scores.csv [code/pipeline/05_igt_validation.R]
# L_pm_filtered.rds [code/pipeline/01b_filter_cells.R]
# igt1_96_..._ADTonly.Rds [primary input Seurat object]
library(dplyr)
library(tidyr)
library(ggplot2)
library(pheatmap)Panels A and B both use the cached per-IGT cosine score matrix:
data_path <- "data/"
figure_path <- "figures/final-selected/Figure S1/"
gp_label <- function(x) sub("^K(\\d+)$", "GP\\1", x)
# ============================================================
# S1A/S1B: load the cached per-IGT cosine score matrix
# (GPs x IGTs; produced by code/pipeline/05_igt_validation.R)
# ============================================================
score_mat <- as.matrix(read.csv(paste0(data_path, "igt_specific_cosine_scores.csv"), row.names = 1, check.names = FALSE))(A) Cumulative GPs validated as IGTs are added
# ============================================================
# S1A: cumulative number of GPs validated as IGTs are added, in IGT-index order
# ============================================================
igt_idx <- as.integer(gsub("^IGT", "", colnames(score_mat)))
o <- order(igt_idx)
score_mat_ord <- score_mat[, o, drop = FALSE]
cum_validated_counts <- function(score_mat_ord, threshold) {
validated <- score_mat_ord >= threshold
ever_validated <- t(apply(validated, 1, cummax)) # 200 x nIGT logical
colSums(ever_validated)
}
plot_df_a <- lapply(thresholds, function(t) {
y <- cum_validated_counts(score_mat_ord, t)
data.frame(n_IGTs_included = seq_along(y), validated_GPs = y, threshold = factor(t))
}) %>% bind_rows()
p_S1A <- ggplot(plot_df_a, aes(x = n_IGTs_included, y = validated_GPs, color = threshold)) +
geom_line(linewidth = 1) +
geom_point(size = 1) +
labs(x = "Number of IGTs included (in IGT index order)", y = "Number of validated GPs (cumulative union)", color = "Threshold") +
theme_minimal() +
scale_color_brewer(palette = "Set1") +
scale_x_continuous(breaks = seq(0, ncol(score_mat_ord), by = 5)) +
scale_y_continuous(breaks = seq(0, max(plot_df_a$validated_GPs), by = 20))
ggsave(paste0(figure_path, "S1A.pdf"), plot = p_S1A, width = 6, height = 4)
Fig. S1A. GPs were tested for reproducibility in individual datasets (IGTs): for each IGT a dataset-specific EBMF factorization was computed and its factors were matched to the global GPs by Hungarian assignment on the cosine similarity of gene-score vectors (restricted to shared genes, with columns scaled), and a GP is counted as “validated” in an IGT when this cosine similarity reaches a given threshold. Adding IGTs one at a time in index order, the curves show the cumulative number of GPs (of 200) validated in at least one IGT included so far, for cosine thresholds of 0.2-0.8.
(B) GPs validated by at least X IGTs
# ============================================================
# S1B: number of GPs validated by at least X IGTs, vs X (log-log)
# ============================================================
thresholds <- seq(0.2, 0.8, by = 0.1)
X_grid <- 1:50
plot_df_b <- tidyr::crossing(threshold = thresholds, X = X_grid) %>%
mutate(n_GP = purrr::map2_int(threshold, X, \(t, x) {
rowSums(score_mat >= t, na.rm = TRUE) |> (\(v) sum(v >= x))()
}))
p_S1B <- ggplot(plot_df_b, aes(x = X, y = n_GP, group = factor(threshold))) +
geom_line() +
geom_point(size = 1) +
scale_y_log10() +
scale_x_log10() +
labs(x = "X (validated by at least X IGTs)", y = "Number of GPs", color = "Threshold") +
aes(color = factor(threshold)) +
theme_minimal() +
scale_color_brewer(palette = "Set1")
ggsave(paste0(figure_path, "S1B.pdf"), plot = p_S1B, width = 6, height = 4)
Fig. S1B. Using the same per-IGT cosine matching, the number of GPs validated by at least X IGTs as a function of X (both axes log-scaled), for thresholds of 0.2-0.8.
(C) Between-IGT variability
# ============================================================
# Load data for S1C/S1D
# ============================================================
L_pm_filtered <- readRDS(paste0(data_path, "L_pm_filtered.rds"))
seurat_meta <- readRDS(paste0(data_path, "igt1_96_withtotalvi20260206_clean_ADTonly.Rds"))@meta.data
seurat_meta_filtered <- seurat_meta[rownames(L_pm_filtered), ]
seurat_meta_filtered_spleen <- seurat_meta_filtered %>% filter(spleen_standard == TRUE)
all_gps <- paste0("K", 1:200)
selected_igts <- names(table(seurat_meta_filtered_spleen$IGT))[table(seurat_meta_filtered_spleen$IGT) >= 500]# ============================================================
# S1C: GP mean-of-IGT-mean-loading vs. variance-of-IGT-mean-loading (spleen)
# ============================================================
common_cells_c <- intersect(rownames(L_pm_filtered), rownames(seurat_meta_filtered_spleen))
L_sub_c <- L_pm_filtered[common_cells_c, ]
igt_vec_c <- seurat_meta_filtered_spleen[common_cells_c, "IGT"]
igt_levels_c <- unique(igt_vec_c)
igt_mat_c <- do.call(rbind, lapply(igt_levels_c, function(igt) colMeans(L_sub_c[igt_vec_c == igt, , drop = FALSE])))
gp_igt_var <- apply(igt_mat_c, 2, var)
gp_overall <- colMeans(igt_mat_c)
gp_stats <- data.frame(GP = colnames(L_sub_c), x = gp_overall, y = gp_igt_var) %>%
arrange(desc(y)) %>%
mutate(label = ifelse(row_number() <= 10, gp_label(as.character(GP)), ""))
p_S1C <- ggplot(gp_stats, aes(x = x, y = y, label = label)) +
geom_point(size = 1.5, alpha = 0.7, color = "steelblue") +
ggrepel::geom_text_repel(seed = 42, size = 3, box.padding = 0.4, max.overlaps = Inf, segment.color = "grey50") +
cowplot::theme_cowplot() +
labs(
title = "GP Mean of IGT Mean Loading vs. Between-IGT VAR",
x = "Mean of IGT Mean Loading",
y = "Variance of IGT Mean Loading"
)
ggsave(paste0(figure_path, "S1C.pdf"), plot = p_S1C, width = 6, height = 5, dpi = 300)
Fig. S1C. Between-IGT variability of GP loadings, computed on a standard spleen subset used for this purpose. Each GP’s mean loading is computed within every IGT; each point is a GP, plotting the mean across IGTs of these per-IGT mean loadings (x-axis) against their variance across IGTs (y-axis). The ten GPs with the highest between-IGT variance are labeled.
(D) Top-variance GP heatmap
# ============================================================
# S1D: heatmap of per-IGT mean loading (IGTs with >= 500 spleen-standard cells
# only) for the top-10 between-IGT-variance GPs over those same IGTs
# ============================================================
spleen_cells_act <- intersect(rownames(L_pm_filtered), rownames(seurat_meta_filtered_spleen))
igt_vec_act <- seurat_meta_filtered_spleen[spleen_cells_act, "IGT"]
igt_mean_mat_act <- do.call(rbind, lapply(selected_igts, function(igt) {
cells_i <- spleen_cells_act[igt_vec_act == igt]
colMeans(L_pm_filtered[cells_i, all_gps, drop = FALSE])
}))
rownames(igt_mean_mat_act) <- selected_igts
# Ranked on S1C's `gp_igt_var` (variance of per-IGT mean loading over all 35
# spleen-standard IGTs) so that S1D's 10 rows ARE S1C's 10 labelled GPs.
#
# This is a deliberate departure from the published panel. Figure_batch.R
# recomputed the variance here on the >= 500-cell subset only, so its S1D
# ranked on 18 IGTs while its S1C labelled on 35 -- and the two disagreed:
# GP2 is 7th on the subset but 11th overall, GP25 is 6th overall but 23rd on
# the subset, so the published S1D shows GP2 while the published S1C labels
# GP25. Readers cannot follow "the top ten from (C)" across that gap, so we
# rank both panels the same way. The >= 500-cell restriction still applies to
# the IGT *columns* drawn below, where it keeps noisy small-IGT means out of
# the heatmap.
top10_var_gps <- names(sort(gp_igt_var, decreasing = TRUE))[1:10]
plot_mat <- t(igt_mean_mat_act[, top10_var_gps, drop = FALSE])
plot_mat[plot_mat < 0] <- 0
rownames(plot_mat) <- gp_label(rownames(plot_mat))
pdf(paste0(figure_path, "S1D.pdf"), width = 5, height = 5)
pheatmap(
plot_mat,
cluster_rows = TRUE,
cluster_cols = FALSE,
main = "Top 10 GPs by Variance of IGT Mean Loading",
color = colorRampPalette(c("white", "red"))(100),
border_color = "white",
fontsize_row = 8,
angle_col = 45
)
dev.off()
Fig. S1D. Heatmap of the per-IGT mean loading for the ten GPs with the highest between-IGT variance in (C) – the same ten GPs labelled there, ranked on the same all-IGT variance. Columns are restricted to the IGTs with >= 500 standard-spleen cells, so that no column is a mean over a handful of cells. Rows (GPs) are hierarchically clustered; color runs from white (low) to red (high mean loading).
(E) Active-gene vs. active-cell proportion per GP
# ============================================================
# S1E: active-gene vs active-cell scatter per GP, using the SAME hard-threshold
# definitions as Figure 2 (per-GP-normalized): number of active genes = count of
# genes with |score| > 0.25 of the GP's max; proportion of active cells = fraction
# of cells with loading > 0.1 of the GP's max. Non-thymocyte cells, matching
# Figure 2. (Replaces the earlier EBMF-sparsity-prior version.)
# ============================================================
non_thymo_s1e <- seurat_meta_filtered$cellID[seurat_meta_filtered$annotation_level1 != "thymocyte"]
L_s1e <- L_pm_filtered[non_thymo_s1e, ]
L_norm_s1e <- L_s1e / matrix(apply(L_s1e, 2, max), nrow = nrow(L_s1e), ncol = ncol(L_s1e), byrow = TRUE)
prop_cells <- colSums(L_norm_s1e > 0.1) / nrow(L_norm_s1e) # proportion of active cells per GP
F_s1e <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
F_norm_s1e <- F_s1e / matrix(apply(F_s1e, 2, function(x) max(abs(x))), nrow = nrow(F_s1e), ncol = ncol(F_s1e), byrow = TRUE)
n_genes_act <- colSums(abs(F_norm_s1e) > 0.25) # number of active genes per GP
scatter_df_s1e <- data.frame(n_genes = n_genes_act, prop_cells = prop_cells)
pct_breaks <- c(0.0001, 0.001, 0.01, 0.05, 0.1, 0.3, 0.5, 1)
p_S1E <- ggplot(scatter_df_s1e, aes(x = n_genes, y = prop_cells)) +
geom_point(size = 2, alpha = 0.7, color = "steelblue") +
scale_x_log10(labels = scales::label_comma()) +
scale_y_log10(breaks = pct_breaks, labels = function(x) paste0(x * 100, "%")) +
annotation_logticks(sides = "bl") +
labs(
x = "Number of active genes per GP (log scale)",
y = "Proportion of active cells per GP (log scale)",
title = "Active genes vs. active-cell proportion per GP"
) +
theme_minimal(base_size = 13)
ggsave(paste0(figure_path, "S1E.pdf"), plot = p_S1E, width = 6, height = 5, dpi = 300)
Fig. S1E. For each GP, the number of active genes (x-axis) versus the proportion of active cells (y-axis), using the same hard-threshold definitions as Figure 2: a gene is active in a GP if its per-GP-normalized score exceeds 0.25 in absolute value, and a cell is active if its per-GP-normalized loading exceeds 0.1; computed over non-thymocyte cells. Both axes on log scale; each dot is one of the 200 GPs.
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