Last updated: 2026-07-24

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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 98d2924 Ziang Zhang 2026-07-24 Reword Figure S5 page for a publication audience
html 7ddbdb4 Ziang Zhang 2026-07-24 Build site.
Rmd b138063 Ziang Zhang 2026-07-24 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

Figure S5 asks whether the fine-grained cluster-level patterns captured by the 200 EBMF gene programs are recovered by an independent factorization. It compares each EBMF gene program with a matched program from RQVI, an alternative matrix factorization of the same cells run across 10 random-seed initializations (256 programs per seed). The two heatmaps share the same row and column order and use a white-to-blue scale of per-program relative loading on [0, 1], with broad lineage annotations above the columns.

Cells are the L_pm_filtered gene-program loadings (cells passing the iterative total-loading filter), restricted to non-thymocytes (annotation_level1 != "thymocyte", all conditions) and to cells that also carry an RQVI loading. EBMF and RQVI loadings are averaged within annotation_level2 on these common cells, and each RQVI program is shown on the row of its matched EBMF program. EBMF factors F1–F200 correspond to gene programs GP1–GP200.

Figure S5

Version Author Date
9398c72 Ziang Zhang 2026-07-23

Fig. S5. Cluster-level activity profiles of EBMF gene programs and matched RQVI programs. EBMF and RQVI program loadings were averaged within 107 fine-grained annotation_level2 clusters, using all non-thymocyte cells that carry both an EBMF and an RQVI loading (n = 629,551 cells). The left heatmap shows the 200 EBMF gene programs ordered by hierarchical clustering of their cluster-level profiles. The right heatmap shows, on the same row and column order, the RQVI program matched to each EBMF program by a one-to-one maximum-correlation assignment over all 2,560 candidate programs (10 random-seed runs, 256 programs each). Loadings were independently rescaled to [0, 1] within each program for display; broad lineage annotations are shown above the columns. The median per-program Pearson correlation across the displayed clusters was 0.766, and 92.5% of programs had r >= 0.5. Standalone left/right panels and the shared colorbar are saved under figures/generated/Figure S5/S5_subfigures/.

How the figure is made

Three steps, run from the repository root:

Rscript script/FigureS5.R          # cluster-mean matrices + column/palette metadata
python  script/FigureS5_rematch.py # one-to-one EBMF-RQVI program matching
python  script/FigureS5_plot.py    # heatmaps

The Python steps require matplotlib, pandas, scipy, h5py, and numpy. The RQVI cell-level loadings are read as input from data/, which is a git-ignored symlink and is not tracked here.

Cluster means

script/FigureS5.R builds the raw annotation_level2 cluster-mean matrices for the EBMF and RQVI programs on the common non-thymocyte cells, together with the column order (Figure-1 level1 lineage order, alphabetical within each lineage) and the lineage color palette.

# Figure S5 (data step): cluster-mean matrices for the EBMF vs RQVI gene-program
# comparison across annotation_level2 clusters.
#
#   * Cells: L_pm_filtered gene-program loadings (cells passing the iterative
#     total-loading filter), restricted to non-thymocytes (annotation_level1 !=
#     "thymocyte", all conditions) and to cells that also carry an RQVI loading
#     ("common cells").
#   * EBMF matrix: flashier loadings L_pm_filtered (GP1..GP200), averaged within
#     annotation_level2 on the common cells.
#   * RQVI matrix: RQVI program loadings, averaged within annotation_level2 on the
#     same common cells, with each program placed under the column of its matched
#     EBMF program. EBMF factor F_k corresponds to gene program GP_k.
#   * Columns (level2 clusters) are ordered by the Figure-1 level1 lineage order,
#     alphabetically within each lineage.
#
# This script writes the raw cluster-mean matrices and column/palette metadata.
# The one-to-one matching is computed by script/FigureS5_rematch.py; row ordering,
# per-program [0,1] scaling, and the heatmaps by script/FigureS5_plot.py.

suppressPackageStartupMessages({
  library(arrow)
  library(data.table)
  library(ZemmourLib)
})

if (!file.exists("code/R/setup_data.R")) {
  stop("Run this script from the immgenT-GP-analysis repository root.")
}
source("code/R/setup_data.R")

outdir <- "figures/generated/Figure S5"
dir.create(outdir, recursive = TRUE, showWarnings = FALSE)

PKG <- "data/rqvi_loading/RQVI_EBMF_heatmap_data_v1/data"
parquet_path <- file.path(PKG, "rqvi_matched_200_cell_loadings.parquet")
matches_path <- file.path(PKG, "ebmf_rqvi_multiseed_level2_one_to_one_matches.csv")
level1_order <- c("CD8", "CD4", "Treg", "gdT", "CD8aa", "Tz", "DN", "DP")

## ---- EBMF loadings + metadata ----
gp <- load_gp_data()
L  <- gp$L_pm_filtered
colnames(L) <- paste0("GP", seq_len(ncol(L)))
meta <- gp$seurat_meta_filtered
stopifnot(identical(rownames(L), rownames(meta)), ncol(L) == 200L)

nonthy <- meta$annotation_level1 != "thymocyte"
if (anyNA(nonthy)) stop("annotation_level1 has missing values.")

## ---- matched RQVI cell loadings (200 programs) ----
pq   <- as.data.frame(arrow::read_parquet(parquet_path))
prog <- setdiff(colnames(pq), "cell_id")
stopifnot(length(prog) == 200L)
matches <- fread(matches_path)
f_for <- matches$ebmf_factor[match(prog, matches$rqvi_candidate)]   # "F<k>" per program
if (anyNA(f_for) || length(unique(f_for)) != 200L) {
  stop("Could not map every RQVI program column to a unique EBMF factor.")
}

## ---- common cells (non-thymocyte, in L_pm, and with RQVI loading) ----
our_nonthy_ids <- rownames(L)[nonthy]
common_ids <- our_nonthy_ids[our_nonthy_ids %in% pq$cell_id]
grp <- factor(as.character(meta$annotation_level2[match(common_ids, rownames(meta))]))
grp <- droplevels(grp)
if (anyNA(grp) || any(as.character(grp) == "")) stop("Missing level2 labels on common cells.")

message(sprintf("common non-thymocyte cells: %d (healthy %d / non-healthy %d); level2 clusters: %d",
                length(common_ids),
                sum(meta$condition_broad[match(common_ids, rownames(meta))] == "healthy"),
                sum(meta$condition_broad[match(common_ids, rownames(meta))] != "healthy"),
                nlevels(grp)))

## ---- EBMF cluster means (columns F1..F200) ----
Lc <- L[common_ids, , drop = FALSE]
colnames(Lc) <- paste0("F", seq_len(ncol(Lc)))
esum <- rowsum(Lc, grp)
ecnt <- as.integer(table(grp)[rownames(esum)])
ebmf_means <- sweep(esum, 1L, ecnt, "/")                 # K x 200

## ---- matched-RQVI cluster means (columns F1..F200, same pairing) ----
pqc <- as.matrix(pq[match(common_ids, pq$cell_id), prog, drop = FALSE])
colnames(pqc) <- f_for
pqc <- pqc[, paste0("F", seq_len(200)), drop = FALSE]     # reorder to F1..F200
rsum <- rowsum(pqc, grp)
rcnt <- as.integer(table(grp)[rownames(rsum)])
rqvi_means <- sweep(rsum, 1L, rcnt, "/")

stopifnot(identical(rownames(ebmf_means), rownames(rqvi_means)),
          identical(ecnt, rcnt),                           # same cells -> same counts
          nrow(ebmf_means) == 107L, ncol(ebmf_means) == 200L)

## ---- level2 column order (Figure-1 level1 order, alphabetical within) ----
l2  <- rownames(ebmf_means)
lmap <- unique(data.frame(level2 = as.character(meta$annotation_level2),
                          level1 = as.character(meta$annotation_level1),
                          stringsAsFactors = FALSE))
if (anyDuplicated(lmap$level2)) stop("A level2 label maps to multiple level1 labels.")
lin <- lmap$level1[match(l2, lmap$level2)]
if (anyNA(lin) || !all(lin %in% level1_order)) stop("Unexpected level1 lineage among clusters.")
ord <- order(match(lin, level1_order), l2)
cluster_order <- data.frame(
  level2_cluster = l2[ord],
  level1         = lin[ord],
  display_column = seq_along(ord) - 1L,
  n_cells        = ecnt[ord],
  stringsAsFactors = FALSE
)

## ---- level1 palette (our canonical colors) as hex ----
l1pal <- ZemmourLib::immgent_colors$level1
hex <- vapply(l1pal, function(cc) {
  v <- grDevices::col2rgb(cc)
  grDevices::rgb(v[1], v[2], v[3], maxColorValue = 255)
}, character(1))
pal_df <- data.frame(level1 = names(hex), color = unname(hex), stringsAsFactors = FALSE)

## ---- write outputs ----
# cell -> level1/level2 for all L_pm_filtered cells; consumed by FigureS5_rematch.py
# to define common cells and clusters (avoids any machine-specific path).
fwrite(data.frame(
  cellID            = rownames(L),
  annotation_level1 = as.character(meta$annotation_level1),
  annotation_level2 = as.character(meta$annotation_level2),
  stringsAsFactors  = FALSE
), file.path(outdir, "S5_cell_metadata.csv.gz"))

fwrite(data.frame(level2_cluster = rownames(ebmf_means), ebmf_means, check.names = FALSE),
       file.path(outdir, "S5_ebmf_raw_means_level2.csv"))
fwrite(data.frame(level2_cluster = rownames(rqvi_means), rqvi_means, check.names = FALSE),
       file.path(outdir, "S5_rqvi_matched_raw_means_level2.csv"))
fwrite(cluster_order, file.path(outdir, "S5_cluster_order.csv"))
fwrite(pal_df,        file.path(outdir, "S5_level1_palette.csv"))
fwrite(data.frame(
  metric = c("common_cells", "healthy_cells", "nonhealthy_cells", "level2_clusters",
             "ebmf_factors", "rqvi_matched_programs"),
  value  = c(length(common_ids),
             sum(meta$condition_broad[match(common_ids, rownames(meta))] == "healthy"),
             sum(meta$condition_broad[match(common_ids, rownames(meta))] != "healthy"),
             nlevels(grp), 200L, 200L)
), file.path(outdir, "S5_build_summary.csv"))

message("Wrote Fig S5 data inputs to ", normalizePath(outdir))

Matching

Each program’s mean-loading profile is z-scored across clusters; the signed Pearson correlation between every EBMF program and all 2,560 RQVI candidate programs is computed; candidates with a constant profile are excluded; and a maximum-weight one-to-one assignment (scipy.optimize.linear_sum_assignment) matches each EBMF program to a distinct RQVI program so that the total signed correlation is maximized. The assignment is computed over all common cells (108 annotation_level2 clusters); the figure displays the 107 non-thymocyte clusters. The median per-program correlation is 0.766, with 92.5% at r >= 0.5. Code: script/FigureS5_rematch.py.

Plotting

script/FigureS5_plot.py orders the EBMF rows by hierarchical clustering (average linkage, correlation distance, optimal leaf ordering), rescales every program to [0, 1] across clusters, and draws the two heatmaps with a shared colorbar; the matched RQVI panel reuses the EBMF row order. Code: script/FigureS5_plot.py.


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: Asia/Shanghai
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