Last updated: 2026-07-27
Checks: 7 0
Knit directory:
immgenT-GP-analysis/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.7.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(1) was run prior to running the
code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version a4fc6a7. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .claude/
Ignored: analysis/.DS_Store
Ignored: analysis/.Rhistory
Ignored: analysis/assets/.DS_Store
Ignored: code/.DS_Store
Ignored: code/other/topic_flashier_20250212.R
Ignored: code/other/topic_wrapper_20250215_alldata_backfit.sh
Ignored: data
Ignored: experiments/
Ignored: figures/.DS_Store
Ignored: figures/Previous/.DS_Store
Ignored: figures/Previous/bits/.DS_Store
Ignored: figures/Previous/bits/Figure 1/.DS_Store
Ignored: figures/Previous/bits/Figure 2/.DS_Store
Ignored: figures/Previous/bits/Figure 3/.DS_Store
Ignored: figures/Previous/bits/Figure 4/.DS_Store
Ignored: figures/Previous/bits/Figure 6/.DS_Store
Ignored: figures/Previous/bits/Figure 7/.DS_Store
Ignored: figures/Previous/bits/Figure S1/.DS_Store
Ignored: figures/Previous/bits/Figure S2/.DS_Store
Ignored: figures/Previous/bits/Figure S3/.DS_Store
Ignored: figures/Previous/bits/Figure S6/.DS_Store
Ignored: figures/Previous/bits/Figure S7/.DS_Store
Ignored: figures/final-selected/.DS_Store
Ignored: figures/final-selected/Figure 1/.DS_Store
Ignored: figures/final-selected/Figure 2/.DS_Store
Ignored: figures/final-selected/Figure S4/.DS_Store
Ignored: log/
Ignored: output/Figure2/
Ignored: output/FigureS5/S5_cell_metadata.csv.gz
Ignored: plan/
Ignored: reorder/
Ignored: tables/
Ignored: tmp/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/Methods_FlashierFit.Rmd)
and HTML (docs/Methods_FlashierFit.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 |
|---|---|---|---|---|
| html | adaef21 | Ziang Zhang | 2026-07-27 | Build site: panel fixes and PDF-derived assets |
| html | 5b19858 | Ziang Zhang | 2026-07-27 | Build site. |
| Rmd | 57b12e3 | Ziang Zhang | 2026-07-27 | Publish the Methods page rewrite; ignore tables/ |
| html | 57b12e3 | Ziang Zhang | 2026-07-27 | Publish the Methods page rewrite; ignore tables/ |
| Rmd | cba8dd4 | Ziang Zhang | 2026-07-12 | Add Methods page: Flashier matrix factorization |
| html | cba8dd4 | Ziang Zhang | 2026-07-12 | Add Methods page: Flashier matrix factorization |
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.
The script takes three command-line arguments: the path to a Seurat
object (.Rds), an output directory, and a flag for whether
to run backfitting. After loading the required libraries, the Seurat
object is read into memory as so. This object contains the
raw count matrix and cell-level metadata for the full ImmGen-T
dataset.
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)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.
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']]$data)Before factorization, we remove genes that would confound or dilute the biological signal:
Trbv, Trav,
Trgv, Trdv, …): highly variable but reflect
V(D)J recombination, not transcriptional programs.mt-): reflect
cell quality rather than biology.Rpl,
Rps, Mrpl, Mrps,
Rsl): ubiquitously expressed housekeeping genes.Gm…,
…Rik, …-ps): annotation artifacts with
unreliable quantification.tcr_genes = grepl(rownames(so), pattern = "Trbv|Trbd|Trbj|Trbc|Trav|Traj|Trac|Trgv|Trgd|Trgj|Trgc|Trdv|Trdj|Trdc")
gm_rik_genes = grepl(rownames(so), pattern = "Gm|Rik$|\\-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]To set the noise level parameter S for
flashier, we estimate the standard deviation of
log-normalized Poisson noise. This gives a data-driven estimate of the
baseline technical noise floor, passed to flash() as the
S argument.
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 scaleWe call flash() with prior families chosen to reflect
the expected structure of the loadings and factors:
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.fit = flash(shifted_log_counts,
ebnm_fn = c(ebnm_point_exponential, ebnm_point_laplace),
var_type = 2,
S = s1,
backfit = TRUE,
greedy_Kmax = 200L)
saveRDS(fit, file = sprintf("%s/fit.Rds", output_dir))The fitted object is saved as fit.Rds and used for all
downstream GP analyses on this site.
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