Last updated: 2026-07-27
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6447bf7 | Ziang Zhang | 2026-07-27 | Remove the unused CLR ADT path; name the LogNormalize matrix unambiguously |
| html | 5b19858 | Ziang Zhang | 2026-07-27 | Build site. |
| Rmd | f0c481d | Ziang Zhang | 2026-07-14 | Document CITE-seq fixed-loading EBMF workflow |
| html | f0c481d | Ziang Zhang | 2026-07-14 | Document CITE-seq fixed-loading EBMF workflow |
This page documents the CITE-seq protein factorization used to connect surface proteins to the scRNA-derived gene programs (GPs). The workflow reads the provided Seurat object, extracts and log-normalizes its ADT assay, and uses the cell loadings learned from the scRNA-seq EBMF fit as fixed covariates to estimate a protein score for each GP.
Let \(Y\) be the cells-by-proteins matrix of log-normalized ADT measurements and let \(L\) be the cells-by-GPs loading matrix from the scRNA-seq fit. We estimate the protein-by-GPs score matrix \(U\) in
\[ Y \approx L U^T, \]
while keeping \(L\) fixed. An
ordinary least-squares projection initializes \(U\); flashier then refines the
protein scores under point-Laplace priors and protein-specific residual
variances.
The complete cluster-scale source scripts are:
The script takes the path to a provided Seurat object and an analysis
directory. After loading the required packages, it reads the object and
its cell metadata. The object must contain an ADT assay and
the cellID, cite_seq, and
nCount_ADT metadata columns.
library(flashier)
library(Seurat)
seurat_obj <- readRDS(path_to_seurat_object)
seurat_meta <- seurat_obj@meta.dataThe ADT assay is stored with proteins in rows and cells in columns.
We use Seurat LogNormalize with the rounded mean ADT
library size as the scale factor, then transpose the result so that
cells are rows and proteins are columns. This is the only ADT
normalization used anywhere in this project:
protein_mat_normalized_lognorm.rds is the single protein
matrix read by the fit below, by the protein positivity thresholds, and
by every figure and table that shows surface-protein levels. No
CLR-normalized ADT matrix is used.
adt_scale_factor <- round(mean(seurat_meta$nCount_ADT, na.rm = TRUE))
seurat_obj <- NormalizeData(
seurat_obj,
assay = "ADT",
normalization.method = "LogNormalize",
scale.factor = adt_scale_factor
)
protein_mat_normalized_lognorm <- t(seurat_obj[["ADT"]]$data)
saveRDS(
protein_mat_normalized_lognorm,
file.path(data_path, "protein_mat_normalized_lognorm.rds")
)For cell \(i\) and protein \(j\), the normalized value is
\[ Y_{ij} = \log\left(1 + \frac{C_{ij}}{\sum_q C_{iq}} \times s\right), \]
where \(C_{ij}\) is the raw ADT
count and \(s\) is
adt_scale_factor.
Only cells marked cite_seq == TRUE are eligible. Their
cell IDs are intersected explicitly with both the normalized protein
matrix and the scRNA loading matrix, then both matrices are placed in
the same order before fitting.
cells_measured <- seurat_meta$cellID[
!is.na(seurat_meta$cite_seq) & seurat_meta$cite_seq
]
cells_used <- cells_measured[
cells_measured %in% rownames(protein_mat_normalized_lognorm) &
cells_measured %in% rownames(scRNA_result$L_pm)
]
L_mat <- scRNA_result$L_pm[cells_used, , drop = FALSE]
Y_mat <- as.matrix(
protein_mat_normalized_lognorm[cells_used, , drop = FALSE]
)With \(L\) fixed, the least-squares initialization is
\[ \hat U^T = (L^T L)^{-1} L^T Y. \]
The implementation uses a QR decomposition instead of forming the inverse explicitly.
qrL <- qr(L_mat)
U_t <- qr.coef(qrL, Y_mat)
U <- t(U_t)
saveRDS(U, file.path(data_path, "protein_projection_OLS_lognorm.rds"))The OLS protein scores initialize flashier. The cell
loading for every GP is then fixed, so backfitting updates the protein
scores and residual variances but does not alter the scRNA-derived
cellular programs.
ebnm_point_laplace allows each protein score to be
exactly zero or positive or negative.var_type = 2 estimates a separate residual variance for
every protein.flash_factors_fix(..., which_dim = "loadings") fixes
all columns of \(L\).flash_fixed_loading <- flash_init(Y_mat, var_type = 2) |>
flash_set_verbose(1) |>
flash_factors_init(
list(L_mat, U),
ebnm_fn = ebnm_point_laplace
) |>
flash_factors_fix(
kset = seq_len(ncol(L_mat)),
which_dim = "loadings"
)The fit is checkpointed after 20, 40, 80, 120, 160, and 200 cumulative iterations. Extrapolation is disabled for the first two 20-iteration stages and enabled for each subsequent 40-iteration stage.
| Cumulative iteration | Additional iterations | Extrapolate |
|---|---|---|
| 20 | 20 | No |
| 40 | 20 | No |
| 80 | 40 | Yes |
| 120 | 40 | Yes |
| 160 | 40 | Yes |
| 200 | 40 | Yes |
checkpoint_schedule <- data.frame(
cumulative_iterations = c(20L, 40L, 80L, 120L, 160L, 200L),
additional_iterations = c(20L, 20L, 40L, 40L, 40L, 40L),
extrapolate = c(FALSE, FALSE, TRUE, TRUE, TRUE, TRUE)
)
for (i in seq_len(nrow(checkpoint_schedule))) {
flash_fixed_loading <- flash_backfit(
flash_fixed_loading,
extrapolate = checkpoint_schedule$extrapolate[[i]],
maxiter = checkpoint_schedule$additional_iterations[[i]],
verbose = 2
)
# The source script saves both the full fit and a compact summary here.
}The final compact output,
protein_flash_selected_summary_lognorm_backfit200.rds,
contains L_pm, F_pm, pve,
elbo, and residuals_sd. This is the
protein-program matrix used by Figure 6, Figure S6, and the
protein-related Extended Data tables.
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