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 you have one of these problems:

  • pySCENIC, arboreto, pycisTopic or pycistarget will not install cleanly on a modern Python stack.
  • AUCell or cisTarget is becoming a runtime or memory bottleneck.
  • You need deterministic CPU execution under a fixed seed.
  • You want a single Python package covering GRN, AUCell, motif support, topics, enhancer links and eRegulons.

Minimal Adoption Test

Run one small dataset first:

pip install rustscenic
python -m rustscenic.quickstart

Then run your own AnnData object through GRN plus AUCell:

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.