Lab Adoption

This page is for collaborators deciding whether RustScenic is worth testing on their own data.

When To Try It

RustScenic is worth testing if one of these problems is blocking your analysis:

  • pySCENIC, arboreto, pycisTopic or pycistarget will not install cleanly on a modern Python stack.
  • AUCell, cisTarget, enhancer linking or GRN inference is becoming a runtime or memory bottleneck.
  • You need reproducible CPU runs with recorded seeds, thread counts and settings.
  • You want a single Python package covering GRN, AUCell, motif support, topics, enhancer links and eRegulons.

Public Proof Points

Use these when introducing the package to a lab or collaborator:

Proof point Evidence
Released v0.5.0, with Python 3.10 to 3.13 release wheels.
Faster tested path 11x to 52x faster than SCENIC+ for selected stages on sampled data, measured on one machine.
Memory measurement v0.5.0 candidate: about 81% less peak physical memory than arboreto in a controlled 20,000-cell comparison. See benchmark scope.
Lab artefact Collaborator human brain workflow recovered 16 of 17 expected brain transcription factors.

Minimal Adoption Test

Run one small dataset first:

pip install rustscenic
python -m rustscenic.quickstart

Then infer a gene network from your own AnnData object. The quickstart adds activity scoring:

import anndata as ad
import rustscenic.data
import rustscenic.grn
import rustscenic.aucell

adata = ad.read_h5ad("your_data.h5ad")
tfs = rustscenic.data.tfs("hs")
grn = rustscenic.grn.infer(adata, tf_names=tfs, n_estimators=500, seed=777)

Evidence To Record

For a useful adoption report, record:

  • Dataset name and source.
  • Number of cells, genes, peaks and transcription factors.
  • RustScenic version.
  • Python version and operating system.
  • Command or script.
  • Wall time and peak memory if available.
  • Output checks: number of GRN edges, regulons, AUCell matrix shape, non-empty motif results.
  • Biological checks: expected marker TFs and their expected cell types.

What Counts As A Strong Adoption Result

A strong report does not need to prove RustScenic is better than every reference tool. It should prove:

  • The tool installs.
  • It runs on real data without manual source edits.
  • Outputs are non-empty and shape-correct.
  • Known biological controls are recovered or failures are explained.
  • The command is reproducible by another person.

Best First Dataset

Use a small real dataset before a full atlas. The most useful first adoption run has enough cells to show biological signal, but not so many that debugging becomes a compute problem. A good first target is 1,000 to 5,000 cells with a known set of marker TFs.