Last updated: 2026-08-05

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Knit directory: immgenT-GP-analysis/analysis/

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/FigureS5.Rmd) and HTML (docs/FigureS5.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
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 6 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.

This figure was split out of Extended Data Figure 6 on 2026-07-30. It was that figure’s first panel, and before 2026-07-28 the published Figure 6b. It now stands alone in the Extended Data Figure 5 slot that main Figure 7b vacated, and Extended Data Figure 6’s remaining panels each dropped one letter (its b-g are now a-f).

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")

# A figure script here was once seen to exit 0 with a complete log and write
# nothing at all (see script/README.md, "A re-run can silently not write"), so
# record when this run started and 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()

Version Author Date
ac650a0 Ziang Zhang 2026-07-30

Extended Data Fig. 5. Heatmap of scaled protein scores for each GP (the re-estimated protein matrix U), with the protein scores in each GP scaled so its maximum |score| = 1. We focused on the 47 proteins that performed best in the immgenT CITE-seq dataset (isotype and low-quality proteins removed). Seventeen GPs lacked meaningful protein correlates – every protein score is exactly zero – and were omitted. Four putative contamination programs (GP40, GP50, GP55 and GP188) were also removed, leaving 179 GPs (200 - 17 - 4 = 179); 47 proteins (rows) x 179 GPs (columns). Entries with an absolute score below 0.5 are shown in white, with color running from blue (-1) through white to red (+1). GP columns are ordered from the most to the fewest visible proteins. Protein rows are ordered by the position of their rightmost visible GP, placing proteins that extend into the sparse right side first and exposing a triangular support boundary; visible count and a rarity-weighted support score break ties. This triangular-first row order is not monotone in per-protein sparsity. GP labels are vertical and sized to the available heatmap-cell width; protein labels are sized to the available row height.


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