Last updated: 2026-09-04

Checks: 7 0

Knit directory: immgenT-GP-analysis/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(1)

The command set.seed(1) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 2344a4a

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 2344a4a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .claude/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/.Rhistory
    Ignored:    analysis/assets/.DS_Store
    Ignored:    captions/
    Ignored:    code/.DS_Store
    Ignored:    code/other/topic_flashier_20250212.R
    Ignored:    code/other/topic_wrapper_20250215_alldata_backfit.sh
    Ignored:    data
    Ignored:    experiments/
    Ignored:    figures/.DS_Store
    Ignored:    figures/Previous/.DS_Store
    Ignored:    figures/Previous/bits/.DS_Store
    Ignored:    figures/Previous/bits/Figure 1/.DS_Store
    Ignored:    figures/Previous/bits/Figure 2/.DS_Store
    Ignored:    figures/Previous/bits/Figure 3/.DS_Store
    Ignored:    figures/Previous/bits/Figure 4/.DS_Store
    Ignored:    figures/Previous/bits/Figure 6/.DS_Store
    Ignored:    figures/Previous/bits/Figure 7/.DS_Store
    Ignored:    figures/Previous/bits/Figure S1/.DS_Store
    Ignored:    figures/Previous/bits/Figure S2/.DS_Store
    Ignored:    figures/Previous/bits/Figure S3/.DS_Store
    Ignored:    figures/Previous/bits/Figure S6/.DS_Store
    Ignored:    figures/Previous/bits/Figure S7/.DS_Store
    Ignored:    figures/final-selected/.DS_Store
    Ignored:    figures/final-selected/Figure 1/.DS_Store
    Ignored:    figures/final-selected/Figure 2/.DS_Store
    Ignored:    figures/final-selected/Figure 4/.DS_Store
    Ignored:    figures/final-selected/Figure S1/.DS_Store
    Ignored:    figures/final-selected/Figure S4/.DS_Store
    Ignored:    figures/templates_20260729/
    Ignored:    internal/
    Ignored:    log/
    Ignored:    output/.DS_Store
    Ignored:    output/Figure2/
    Ignored:    output/Figure7b/7b_cell_metadata.csv.gz
    Ignored:    plan/
    Ignored:    tables/
    Ignored:    tmp/

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/FigureS6.Rmd) and HTML (docs/FigureS6.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 2344a4a Ziang Zhang 2026-09-04 Three captions corrected where the published text and the panel disagree
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 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 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 732ac8d Ziang Zhang 2026-07-29 Build site: caption alignment with captions_20260729_final.docx
Rmd 8d73953 Ziang Zhang 2026-07-28 Align captions with captions_20260729_final.docx
html ae21d37 Ziang Zhang 2026-07-28 Build site: republish after the reorder commits
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 7fb2ab0 Ziang Zhang 2026-07-28 Build site: FigureS6 gallery code shows the verified-install write
html f781754 Ziang Zhang 2026-07-28 Build site: FigureS6 caption on omitted unthresholded markers
Rmd aeb91c8 Ziang Zhang 2026-07-28 Panel titles list only the markers the gate actually applied
html 3d96bb2 Ziang Zhang 2026-07-28 Build site: FigureS6 header note on the curated threshold file
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 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
html 92021bf Ziang Zhang 2026-07-02 Build site.
Rmd db7a5cd Ziang Zhang 2026-07-02 Recover 3 data-provenance gaps into pipeline scripts
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

This single-panel figure is produced by script/FigureS6.R, which shares its CITE-seq setup and protein filters with Figure 6 and Extended Data Figure 7 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 S6/"
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

# ============================================================
# s6: 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_s6 <- as.matrix(Protein_F_pm_simplified_no_contamination)
wide_visible_mask_s6 <- abs(wide_matrix_s6) >= sparse_cutoff
wide_gp_visible_count_s6 <- colSums(wide_visible_mask_s6)
wide_protein_visible_count_s6 <- rowSums(wide_visible_mask_s6)
wide_gp_number_s6 <- as.integer(sub("^GP", "", colnames(wide_matrix_s6)))

wide_gp_order_s6 <- order(-wide_gp_visible_count_s6, wide_gp_number_s6)
wide_mask_ordered_cols_s6 <- wide_visible_mask_s6[
  ,
  wide_gp_order_s6,
  drop = FALSE
]
wide_rightmost_visible_gp_s6 <- apply(
  wide_mask_ordered_cols_s6,
  1,
  function(values) max(which(values))
)
wide_rarity_weights_s6 <- seq_len(ncol(wide_mask_ordered_cols_s6))^2
wide_protein_rarity_score_s6 <- as.numeric(
  wide_mask_ordered_cols_s6 %*% wide_rarity_weights_s6
)
wide_protein_order_s6 <- order(
  -wide_rightmost_visible_gp_s6,
  -wide_protein_visible_count_s6,
  -wide_protein_rarity_score_s6,
  rownames(wide_matrix_s6)
)
wide_ordered_matrix_s6 <- wide_matrix_s6[
  wide_protein_order_s6,
  wide_gp_order_s6,
  drop = FALSE
]

pdf(paste0(figure_path, "s6.pdf"), width = 48, height = 14)
pheatmap::pheatmap(
  wide_ordered_matrix_s6,
  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
19c977f Ziang Zhang 2026-09-02

Extended Data Fig. 6. 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):
 [1] vctrs_0.7.3     cli_3.6.6       knitr_1.50      rlang_1.2.0    
 [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