Last updated: 2026-07-28

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Knit directory: immgenT-GP-analysis/analysis/

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Rmd 4c07670 Ziang Zhang 2026-07-28 Reorder Figures 6, S6 and S3; make the ex-S5 figure Figure 7b

Figure 7 (RQVI validates EBMF-derived gene programs with a non-linear deep learning framework) is otherwise produced by our collaborator’s RQVI pipeline, not by this repository: panels 7a and 7c-7g have no code here (7a is a Figma schematic, 7c/7d/7f are Matplotlib output whose source is not in this repo, and 7e/7g trace to RQVI validation code that was scoped out – see script/README.md). This page covers panel 7b only, which is ours.

This panel was Extended Data Figure 5 until 2026-07-28. It was published as two half-panels (S5a, S5b) plus a detached colorbar; it is now a single assembled panel and has moved into the main figure as 7b, replacing the earlier 7B (a cell-level EBMF-vs-RQVI correlation heatmap from the collaborator’s pipeline). Extended Data Figure 5 no longer exists; the remaining Extended Data figures keep their numbers.

Figure 7b 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.

The published caption calls the left-hand programs EBMF factors and the right-hand ones RQVI programs; on this page “program” is used for both.

Figure 7b

Fig. 7b. 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. The assembled panel is saved as 7B.pdf in figures/final-selected/Figure 7/; its two halves and the standalone colorbar are kept as build intermediates in output/Figure7b/.

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 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. 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 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/Figure7b_rematch.py.

Plotting

script/Figure7b_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. 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_1.8.7   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