Last updated: 2026-09-04

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Knit directory: immgenT-GP-analysis/analysis/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Figure7.Rmd) and HTML (docs/Figure7.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 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
html dd76d38 Ziang Zhang 2026-08-19 Build site: Figure 7 and index pages rebuilt
Rmd 801dbf7 Ziang Zhang 2026-08-19 Figure 7 pages: present Figure 7 as one figure spanning EBMF and RQVI
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
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

This page documents Figure 7b. The other panels of Figure 7 (A nonlinear deep-learning framework independently recovers the immgenT gene-program landscape) are produced by the RQVI framework and are documented in the companion repository for RQVI.

Figure 7b asks whether the fine-grained cluster-level patterns captured by the 200 EBMF GPs are recovered by an independent factorization. It compares each EBMF GP with a matched RQVI GP – RQVI being an alternative matrix factorization of the same cells, run across 10 random-seed initializations (256 GPs per seed). The two heatmaps share the same row and column order and use a white-to-blue scale of per-GP relative loading on [0, 1], with broad lineage annotations above the columns.

Cells are the L_pm_filtered GP 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 GP is shown on the row of its matched EBMF GP. In the code and in the intermediate files the EBMF GPs are named F1-F200; those are GP1-GP200.

Figure 7b

Version Author Date
d538aa2 Ziang Zhang 2026-07-28

Fig. 7b. Cluster-level activity profiles of corresponding EBMF and RQVI GPs. EBMF cell loadings (left) and matched RQVI program loadings (right) were averaged within the 107 level-2 clusters.

How the figure is made

Three steps, run from the repository root:

Rscript script/Figure7b.R          # cluster-mean matrices + column/palette metadata
python  script/Figure7b_rematch.py # one-to-one EBMF-RQVI program matching
python  script/Figure7b_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/Figure7b.R builds the raw annotation_level2 cluster-mean matrix for the EBMF GPs 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. The matched RQVI cluster means are produced by the matching step below.

# Figure 7b (data step): EBMF cluster-mean matrix and column/palette metadata 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"). The RQVI cell set is taken from the cell_id index of the
#     RQVI loading table.
#   * EBMF matrix: flashier loadings L_pm_filtered (GP1..GP200), averaged within
#     annotation_level2 on the common cells.
#   * Columns (level2 clusters) are ordered by the Figure-1 level1 lineage order,
#     alphabetically within each lineage.
#
# This script writes the EBMF cluster-mean matrix, a cell->annotation table, and
# the column order + lineage palette. The RQVI cluster means and the one-to-one
# EBMF-RQVI matching are computed by script/Figure7b_rematch.py; row ordering,
# per-program [0,1] scaling, and the heatmaps by script/Figure7b_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 <- "output/Figure7b"   # build intermediates; final panels come from Figure7b_plot.py
dir.create(outdir, recursive = TRUE, showWarnings = FALSE)

PKG <- "data/rqvi_loading/RQVI_EBMF_heatmap_data_v1/data"
rqvi_loading_path <- file.path(PKG, "rqvi_matched_200_cell_loadings.parquet")
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.")

## ---- common cells (non-thymocyte, in L_pm, and with an RQVI loading) ----
rqvi_cell_ids <- as.data.frame(arrow::read_parquet(rqvi_loading_path, col_select = "cell_id"))$cell_id
our_nonthy_ids <- rownames(L)[nonthy]
common_ids <- our_nonthy_ids[our_nonthy_ids %in% rqvi_cell_ids]
grp <- droplevels(factor(as.character(meta$annotation_level2[match(common_ids, rownames(meta))])))
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
stopifnot(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 (canonical lineage 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 Figure7b_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, "7b_cell_metadata.csv.gz"))

fwrite(data.frame(level2_cluster = rownames(ebmf_means), ebmf_means, check.names = FALSE),
       file.path(outdir, "7b_ebmf_raw_means_level2.csv"))
fwrite(cluster_order, file.path(outdir, "7b_cluster_order.csv"))
fwrite(pal_df,        file.path(outdir, "7b_level1_palette.csv"))
fwrite(data.frame(
  metric = c("common_cells", "healthy_cells", "nonhealthy_cells", "level2_clusters", "ebmf_factors"),
  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)
), file.path(outdir, "7b_build_summary.csv"))

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

Matching

Each GP’s mean-loading profile is z-scored across clusters; the signed Pearson correlation between every EBMF GP and all 2,560 candidate RQVI GPs 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 GP to a distinct RQVI GP 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-GP correlation is 0.766, with 92.5% at r >= 0.5. Code: script/Figure7b_rematch.py.

Plotting

script/Figure7b_plot.py orders the EBMF rows by hierarchical clustering (average linkage, correlation distance, optimal leaf ordering), rescales every GP to [0, 1] across clusters, and draws the two heatmaps with a shared colorbar; the matched RQVI panel reuses the EBMF row order. The published PDF and the PNG this page shows come out of the same matplotlib figure in one call, so the two cannot drift apart. Code: script/Figure7b_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: 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