Extended Data Figure 5: Protein programs across GPs
Ziang Zhang
Last updated: 2026-09-10
Checks: 7 0
Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 | bf4f612 | Ziang Zhang | 2026-09-09 | Build site: Extended Data back to 1-8 |
| Rmd | 6f01135 | Ziang Zhang | 2026-09-09 | Pull the tissue figure back out of Extended Data; ED is 1-8 again |
| html | 6f01135 | Ziang Zhang | 2026-09-09 | Pull the tissue figure back out of Extended Data; ED is 1-8 again |
| html | 8c0c07f | Ziang Zhang | 2026-09-09 | Build site: Extended Data Figure 5 on the 32-GP union |
| Rmd | 9fab2f6 | Ziang Zhang | 2026-09-09 | Extended Data Figure 5: show the union of both tissue-associated GP sets |
| html | b0c1d19 | Ziang Zhang | 2026-09-09 | Build site: Extended Data Figure 5b recoloured |
| Rmd | 5be33df | Ziang Zhang | 2026-09-09 | Extended Data Figure 5b: purple is the positive end, green the negative |
| html | a7a481f | Ziang Zhang | 2026-09-09 | Build site: the rebuilt Figure 1d and the Extended Data renumbering |
| Rmd | c233cd8 | Ziang Zhang | 2026-09-09 | Extended Data reorganisation: split the tissue/cluster figure, renumber 3-8 |
| 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 | 5d3b86c | Ziang Zhang | 2026-09-03 | Build site: Extended Data Figure 5 page rebuilt after the recolouring |
| Rmd | 2101847 | Ziang Zhang | 2026-09-03 | Extended Data Figure 5: colour each lineage row on its own |
| html | 19c977f | Ziang Zhang | 2026-09-02 | Build site: Extended Data 5-7 renumbered, Figure S5 page rebuilt |
| Rmd | 1e5721a | Ziang Zhang | 2026-09-02 | Extended Data 5-7 renumbered, and Figure S5 assembled as one stacked figure |
| html | ae03072 | Ziang Zhang | 2026-08-19 | Build site: six pages rebuilt after the prose cleanup |
| Rmd | adc2327 | Ziang Zhang | 2026-08-19 | Site prose: finish taking internal notes off the pages |
| html | eeca07b | Ziang Zhang | 2026-08-05 | Keep pre-refactor provenance in panel comments off the published pages |
| Rmd | 5651d0e | Ziang Zhang | 2026-08-05 | Extended Data tables: reorder to six, rebuild Table 1, drop internal notes |
| 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 |
| Rmd | c9b020f | Ziang Zhang | 2026-07-30 | Split Figure S6’s protein-program heatmap out as Figure S5 |
| html | d538aa2 | Ziang Zhang | 2026-07-28 | Build site: reordered Figures 6 / S6 / S3 and the new Figure 7b page |
| Rmd | 4c07670 | Ziang Zhang | 2026-07-28 | Reorder Figures 6, S6 and S3; make the ex-S5 figure Figure 7b |
| 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 | ffe285c | Ziang Zhang | 2026-07-27 | Reorganize figures/ and untrack local-only exploration notes |
| html | f7d90e7 | Ziang Zhang | 2026-07-26 | Build site. |
| Rmd | b0b2b2f | Ziang Zhang | 2026-07-26 | Tidy Figure S5 page: name the S5a/S5b/colorbar panels |
| html | fe93d0d | Ziang Zhang | 2026-07-23 | Build site. |
| Rmd | 61de7cb | Ziang Zhang | 2026-07-23 | Reflect single-matching pipeline on the Figure S5 page |
| html | 9862b6d | Ziang Zhang | 2026-07-23 | Build site. |
| Rmd | 98d2924 | Ziang Zhang | 2026-07-23 | Reword Figure S5 page for a publication audience |
| html | 7ddbdb4 | Ziang Zhang | 2026-07-23 | Build site. |
| Rmd | b138063 | Ziang Zhang | 2026-07-23 | Reformat Figure S5 page: lead with the figure, concise methods, link |
| html | 9398c72 | Ziang Zhang | 2026-07-23 | Publish Figure S5 workflowr page |
| Rmd | b9f4f58 | Ziang Zhang | 2026-07-23 | Add Figure S5: EBMF vs matched-RQVI level2-cluster comparison |
This single-panel figure is produced by script/FigureS5.R,
which shares its CITE-seq setup and protein filters with Figure 7 and Extended
Data Figure 6 via code/R/citeseq_shared_setup.R. The
code below is shown for reference (not re-executed on this page, since
the shared setup takes about a minute to load); the image is its
pre-rendered output.
Setup
library(dplyr)
library(pheatmap)
library(Matrix) # protein matrices are dgCMatrix; must be attached for `[` to dispatch
data_path <- "data/"
figure_path <- "figures/final-selected/Figure S5/"
source("code/R/citeseq_shared_setup.R")
# Record when this run started, to assert at the end that the panel is newer.
run_started_at <- Sys.time()Protein-program heatmap
# ============================================================
# s5: sparse protein-program heatmap, contamination GPs removed
# ============================================================
# The normalized protein matrix is derived here rather than in
# citeseq_shared_setup.R because this is its only consumer among the figures.
Protein_F_pm <- Protein_F_pm_raw[!rownames(Protein_F_pm_raw) %in% isotype_proteins, ]
Protein_F_pm <- Protein_F_pm[rownames(Protein_F_pm) %in% good_proteins, ]
Protein_F_pm <- Protein_F_pm[!rownames(Protein_F_pm) %in% exclude_proteins, ]
Protein_F_pm <- Protein_F_pm[!rownames(Protein_F_pm) %in% thy11_proteins, ]
D_lognorm <- diag(1 / apply(Protein_F_pm, 2, function(x) max(abs(x))))
Protein_F_pm <- Protein_F_pm %*% D_lognorm
colnames(Protein_F_pm) <- paste0("GP", 1:ncol(Protein_F_pm))
Protein_F_pm[is.na(Protein_F_pm)] <- 0
threshold_simplified <- 0
keep_rows_simplified <- apply(Protein_F_pm, 1, function(v) any(abs(v) > threshold_simplified, na.rm = TRUE))
Protein_F_pm_simplified <- Protein_F_pm[keep_rows_simplified, , drop = FALSE]
keep_cols_simplified <- apply(Protein_F_pm_simplified, 2, function(v) any(abs(v) > threshold_simplified, na.rm = TRUE))
Protein_F_pm_simplified <- Protein_F_pm_simplified[, keep_cols_simplified, drop = FALSE]
GP_contamination <- c("GP40", "GP50", "GP55", "GP188")
Protein_F_pm_simplified_no_contamination <- Protein_F_pm_simplified[, !colnames(Protein_F_pm_simplified) %in% GP_contamination, drop = FALSE]
sparse_cutoff <- 0.5
bk_sparse <- unique(c(seq(-1, -sparse_cutoff, length.out = 26), seq(-sparse_cutoff, sparse_cutoff, length.out = 51), seq(sparse_cutoff, 1, length.out = 26)))
cols_sparse <- c(colorRampPalette(c("#4575B4", "white"))(25), rep("white", 50), colorRampPalette(c("white", "#D73027"))(25))
# Display proteins as rows and GPs as columns. Order GP columns from most to
# fewest visible proteins. Order protein rows by their rightmost visible GP, so
# proteins extending into the sparse right side appear first and form a
# triangular boundary. Visible count and a rarity-weighted support score provide
# deterministic secondary ordering.
wide_matrix_s5 <- as.matrix(Protein_F_pm_simplified_no_contamination)
wide_visible_mask_s5 <- abs(wide_matrix_s5) >= sparse_cutoff
wide_gp_visible_count_s5 <- colSums(wide_visible_mask_s5)
wide_protein_visible_count_s5 <- rowSums(wide_visible_mask_s5)
wide_gp_number_s5 <- as.integer(sub("^GP", "", colnames(wide_matrix_s5)))
wide_gp_order_s5 <- order(-wide_gp_visible_count_s5, wide_gp_number_s5)
wide_mask_ordered_cols_s5 <- wide_visible_mask_s5[
,
wide_gp_order_s5,
drop = FALSE
]
wide_rightmost_visible_gp_s5 <- apply(
wide_mask_ordered_cols_s5,
1,
function(values) max(which(values))
)
wide_rarity_weights_s5 <- seq_len(ncol(wide_mask_ordered_cols_s5))^2
wide_protein_rarity_score_s5 <- as.numeric(
wide_mask_ordered_cols_s5 %*% wide_rarity_weights_s5
)
wide_protein_order_s5 <- order(
-wide_rightmost_visible_gp_s5,
-wide_protein_visible_count_s5,
-wide_protein_rarity_score_s5,
rownames(wide_matrix_s5)
)
wide_ordered_matrix_s5 <- wide_matrix_s5[
wide_protein_order_s5,
wide_gp_order_s5,
drop = FALSE
]
pdf(paste0(figure_path, "s5.pdf"), width = 48, height = 14)
pheatmap::pheatmap(
wide_ordered_matrix_s5,
main = sprintf(
paste0(
"Protein programs - GP columns, triangular-first protein rows ",
"(|score| >= %.1f; protein-row sparsity is not monotone)"
),
sparse_cutoff
),
color = cols_sparse,
breaks = bk_sparse,
cluster_rows = FALSE,
cluster_cols = FALSE,
border_color = "grey75",
fontsize = 16,
fontsize_row = 16,
fontsize_col = 16,
angle_col = 90,
legend_breaks = c(-1, -sparse_cutoff, 0, sparse_cutoff, 1),
legend_labels = c("-1", "-0.5", "0 (white)", "0.5", "1")
)
dev.off()
Extended Data Fig. 5. Heatmap of scaled protein scores for each GP. We focused on the 47 proteins that performed best in the immgenT CITE-seq dataset.
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):
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[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