Scope¶

RustScenic focuses on the regulatory-network compute path that benefits most from a small Rust-backed Python package: matrix-level inference, per-cell scoring, motif enrichment, topic modelling, enhancer-gene links and eRegulon assembly.

Designed For¶

• Local CPU runs on laptops and workstations.
• Python 3.10 to 3.13 environments.
• Researchers who want fewer moving parts than the legacy SCENIC stack.
• Benchmarked, reproducible workflows with commands and artefacts committed in the repository.

Current Boundary¶

• Motif ranking databases are external inputs because public databases can be hundreds of megabytes to tens of gigabytes.
• GRN edge rankings are independently implemented and can differ from arboreto at fine grain; downstream cell-level agreement is stronger.
• Topic modelling ships both Online VB and collapsed Gibbs paths. The Gibbs path is the stronger sparse scATAC option at larger topic counts.
• Larger real multiome runs and repeated measurements on a second machine are the next benchmark tier.
• Full workflow coverage from raw fragments plus external motif databases is in active validation.

Positioning¶

The strongest current message is direct: RustScenic gives a faster, deterministic regulatory-network compute path with a much simpler install than the legacy stack, and with measured head-to-head speedups on the tested real-data core E2E rows.