Batch-aware, SVD-based pathway-activity inference for single-cell transcriptomics, natively integrated with the Scanpy/AnnData ecosystem. scROMA is a Python implementation of ROMA (Representation and Quantification Of Module Activity) that scores each gene set from the coordinated variation among its member genes, with per-cell activity scores, per-gene contributions, and permutation-based significance. Its batch-aware extension corrects technical variation inside the gene-set subspace rather than across the whole transcriptome, and it ships with a generative simulation framework that produces data with fully specified ground-truth pathway activity.
Install
pip install scroma
# or with uv:
uv pip install scroma # into the active environment
uv add scroma # into a uv project
Requires Python ≥ 3.10 and anndata ≥ 0.12. Tutorial datasets download on first use (via pooch).
Quick start
import scroma
# --- core pathway-activity scoring ---
adata = scroma.datasets.pbmc3k() # downloaded + cached on first use
roma = scroma.ROMA()
roma.adata = adata
roma.gmt = scroma.use_hallmarks() # bundled MSigDB Hallmark gene sets
roma.compute()
roma.summary() # significantly active pathways
# --- batch-aware scoring (correction within the gene-set subspace) ---
broma = scroma.BatchAwareROMA()
broma.adata = adata
broma.gmt = scroma.use_hallmarks()
broma.compute(batch_key="batch", batch_method="harmonylike")
# --- simulation with ground-truth pathway activity ---
sim = scroma.simulate(n_batches=3, seed=42) # AnnData + ground truth in .obsm/.uns
reps = scroma.load_benchmark("multi") # 10 default benchmark replicates
Features
- Two complementary scores per gene set: an L1 overdispersion score and a median-expression shift score, plus per-cell activity scores and per-gene contribution weights.
- Permutation significance with Benjamini–Hochberg correction.
- Batch-aware correction within each gene set (
residualized,contrastive,weighted,centered,ComBat,MNN,harmonylike). - Ground-truth simulator (
scroma.simulate/scroma.load_benchmark) for quantitative benchmarking. - Bundled gene sets (
use_hallmarks,use_reactome,use_progeny) and one-call tutorial datasets (datasets.pbmc3k,datasets.pbmc_ifnb).
Documentation
Full docs and tutorials: https://scroma.readthedocs.io
- Getting started — scoring pathway activity on PBMCs
- Simulated data — visualising ground truth and computing AUROC
- Gene-set weights — which genes drive a pathway
- Pathway-activity clustermap
Citing scROMA
If you use scROMA, please cite the paper (see CITATION.cff).
License
scROMA is released under the GNU GPL v3.
1 maintainer
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