RustScenic¶
Faster, memory-efficient regulatory-network analysis for single-cell and multiome data.
RustScenic provides Rust kernels for GRN inference, regulon activity, motif enrichment, topic modelling, enhancer links and eRegulons through a Python API. It is CPU-first, installable from PyPI and designed for reproducible local runs.
pip install rustscenic
Evidence Snapshot¶
| Signal | Evidence |
|---|---|
| Built | Cross-platform Rust and Python CI, docs build, release smoke checks and nightly real-data validation workflows. |
| Released | Current release v0.4.7; PyPI package with Python 3.10 to 3.13 release wheels plus source distribution. |
| Benchmarked | 11x to 52x faster than SCENIC+ in tested real-data core E2E rows; commands, hardware, runtime, memory and output checks are committed. |
| Memory-scaled | 6.34 GB peak RSS on a 100k-cell four-stage scale check; legacy pySCENIC reports exceed 40 GB on similar workloads. |
| Lab-validated | Huang Lab collaborator artefacts include a 10x human brain GEM-X full monolith run recovering 16/17 expected brain TFs. |
Highlights¶
| Feature | Status |
|---|---|
| Tested real-data speedup | 11x to 52x vs SCENIC+ in core E2E rows |
| Memory scaling | 6.34 GB peak RSS on a 100k-cell four-stage scale check; legacy pySCENIC reports exceed 40 GB on similar workloads |
| Current release | v0.4.7 |
| Python support | 3.10 to 3.13 |
| Core install | pip install rustscenic |
| Runtime model | CPU-first Rust kernels |
| Core path dependencies avoided | Java, dask, CUDA, Snakemake |
| Evidence | Controlled benchmarks plus committed collaborator real-data artefacts |
Benchmark Snapshot¶
| Result | Value |
|---|---|
| Human brain GEM-X 2k total runtime | RustScenic 11.89 s; reference 150.36 s |
| Human brain GEM-X region-to-gene edge-set Jaccard | 1.000 |
| Human brain GEM-X region AUCell mean Pearson | 0.823 |
cisTarget AUC kernel agreement vs ctxcore.recovery.aucs |
Pearson 1.0000 |
The full benchmark matrix includes dataset shape, command path, hardware, runtime, memory and validation metrics. Start with Benchmarks.
Stage Coverage¶
| Stage | RustScenic API | SCENIC ecosystem stage covered |
|---|---|---|
| TF-to-gene GRN | rustscenic.grn.infer |
GRNBoost2-style regulatory-network inference |
| AUCell | rustscenic.aucell.score |
Per-cell regulon activity scoring |
| cisTarget | rustscenic.cistarget.enrich |
Motif enrichment and support filtering |
| Topics | rustscenic.topics.fit, fit_gibbs |
scATAC topic modelling |
| ATAC preprocessing | rustscenic.preproc |
Fragment matrix building and QC |
| Enhancer links | rustscenic.enhancer.link_peaks_to_genes |
Peak-to-gene linking |
| eRegulons | rustscenic.eregulon.build_eregulons |
Enhancer-linked regulon assembly |
| Orchestration | rustscenic.pipeline.run |
Staged workflow across RNA and multiome inputs |