Last updated: 2026-07-12
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immgenT-GP-analysis/analysis/
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This page documents the upstream matrix factorization step that
produces the gene programs (GPs) analyzed throughout this site. The code
is shown for reference; it was run on the UChicago RCC cluster and its
outputs (the fitted flashier object) are the starting point
for all downstream analyses.
We fit an empirical Bayes matrix factorization (EBMF) model to the
full ImmGen-T scRNA-seq dataset using the flashier
R package. The model decomposes the cells × genes expression matrix into
a product of a loading matrix (cells × GPs) and a factor matrix (GPs ×
genes), learning up to 200 gene programs from the data.
Each cell’s counts are log-normalized using a cell-specific scale
factor equal to the dataset-wide mean library size
(mean(nCount_RNA)). This “shifted log” normalization keeps
the data on a comparable scale across cells while preserving count
structure.
Before factorization, we remove genes that would confound or dilute the biological signal:
Trbv, Trav,
Trgv, Trdv, …): highly variable but reflect
V(D)J recombination, not transcriptional programs.mt-): reflect
cell quality rather than biology.Rpl,
Rps, Mrpl, Mrps,
Rsl): ubiquitously expressed housekeeping genes.Gm…,
…Rik, …-ps): annotation artifacts with
unreliable quantification.To set the noise level parameter S for
flashier, we estimate the standard deviation of
log-normalized Poisson noise:
n <- nrow(counts) # number of cells
x <- rpois(1e7, 1/n) # Poisson draws at rate 1/cell
s1 <- sd(log(x + 1)) # SD on the shifted-log scaleThis gives a data-driven estimate of the baseline technical noise
floor, passed to flash() as the S
argument.
fit <- flash(shifted_log_counts,
ebnm_fn = c(ebnm_point_exponential, ebnm_point_laplace),
var_type = 2,
S = s1,
backfit = TRUE,
greedy_Kmax = 200L)ebnm_point_exponential for loadings
(cells): enforces non-negativity, so each cell’s contribution to a GP is
a non-negative weight.ebnm_point_laplace for factors
(genes): allows both positive and negative gene weights within a GP,
capturing genes that are up- or down-regulated together.var_type = 2: estimates a separate
residual variance for each gene (column-wise), accommodating the wide
range of expression variability across genes.backfit = TRUE: after the greedy
initialization adds up to 200 factors, a backfitting pass refines all
factors jointly to improve the overall fit.The fitted object is saved as fit.Rds and used for all
downstream GP analyses on this site.
# David Zemmour
# Usage: Rscript topic_flashier_20250212.R [path_to_seurat_object] [output_dir] [backfit (True/False)]
options(max.print=1000)
options(expressions = 50000)
args = commandArgs(TRUE)
path_to_seurat_object = args[1]
output_dir = args[2]
backfit_option = tolower(args[3])
if (backfit_option %in% c("true", "t")) {
backfit_option = TRUE
} else if (backfit_option %in% c("false", "f")) {
backfit_option = FALSE
}
if (!dir.exists(output_dir)) dir.create(output_dir, recursive = TRUE)
libs = c("fastTopics", "flashier", "Matrix", "Seurat", "BPCells")
sapply(libs, function(x) suppressMessages(library(x, character.only = TRUE, quietly = TRUE)))
so = readRDS(file = path_to_seurat_object)
norm_fac = mean(so$nCount_RNA)
so = NormalizeData(so, assay = "RNA", normalization.method = "LogNormalize", scale.factor = norm_fac)
counts = t(so[['RNA']]$counts)
shifted_log_counts = t(so[['RNA']]$data)
tcr_genes = grepl(rownames(so), pattern = "Trbv|Trbd|Trbj|Trbc|Trav|Traj|Trac|Trgv|Trgd|Trgj|Trgc|Trdv|Trdj|Trdc")
gm_rik_genes = grepl(rownames(so), pattern = "Gm|Rik$|\\-ps$")
ribo_genes = grepl(rownames(so), pattern = "Rpl|Rps|Mrpl|Mrps|Rsl")
mt_genes = grepl(rownames(so), pattern = "^mt-")
genes_not_expressed = rowSums(so[["RNA"]]$counts) == 0
genes_to_keep = !(tcr_genes | gm_rik_genes | ribo_genes | mt_genes | genes_not_expressed)
shifted_log_counts = shifted_log_counts[, genes_to_keep]
counts = counts[, genes_to_keep]
n = nrow(counts)
x = rpois(1e7, 1/n)
s1 = sd(log(x + 1))
fit = flash(shifted_log_counts,
ebnm_fn = c(ebnm_point_exponential, ebnm_point_laplace),
var_type = 2,
S = s1,
backfit = backfit_option,
greedy_Kmax = 200L)
saveRDS(fit, file = sprintf("%s/fit.Rds", output_dir))
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 stringr_1.6.0
[25] compiler_4.5.1 fs_1.6.6 Rcpp_1.1.1-1.1 pkgconfig_2.0.3
[29] later_1.4.4 digest_0.6.39 R6_2.6.1 pillar_1.11.1
[33] magrittr_2.0.5 bslib_0.9.0 tools_4.5.1 cachem_1.1.0