Figure 7b: Cluster-level activity of EBMF GPs and their matched RQVI GPs
Ziang Zhang
Last updated: 2026-08-19
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Knit directory:
immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 (RQVI validates EBMF-derived gene programs with a non-linear deep learning framework) 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 of EBMF GPs and their matched
RQVI GPs. Loadings were averaged within the 107 level 2
clusters of non-thymocyte cells – specifically, all non-thymocyte cells
carrying both an EBMF and an RQVI loading (n = 629,551). Left, the 200
EBMF GPs, ordered by hierarchical clustering of their cluster-level
profiles; right, on the same row and column order, the RQVI GP matched
one-to-one to each EBMF GP by maximum correlation (see Matching, below) – a global assignment over all
2,560 candidates (10 random-seed runs, 256 GPs each), computed on all
shared clusters including the thymocyte one. Loadings are rescaled to
[0, 1] within each GP, and broad lineage annotations are
shown above the columns. The median per-GP Pearson correlation across
clusters was 0.766, and 92.5% of GPs 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 # heatmapsThe 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_1.8.7 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