Quickstart¶

This example uses v0.5.0. It starts with an AnnData RNA matrix, infers a gene network and scores candidate gene sets in each cell. The sets are not motif-filtered or split by correlation sign.

To separate target sets by correlation sign, use add_correlation, build_regulons or add-cor; see the API map. They are not required for the basic example below.

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

adata = ad.read_h5ad("rna.h5ad")
tfs = rustscenic.data.tfs("hs")

grn = rustscenic.grn.infer(
    adata,
    tf_names=tfs,
    n_estimators=500,
    top_targets_per_tf=50,
    seed=777,
)
regulons = {
    tf: group["target"].tolist()
    for tf, group in grn.groupby("TF")
    if len(group) >= 10
}

auc = rustscenic.aucell.score(adata, regulons, top_frac=0.05)
auc.to_parquet("aucell.parquet")

CLI¶

The same core stages are available from the command line:

rustscenic grn \
  --expression data.h5ad \
  --tfs tfs.txt \
  --output grn.parquet

rustscenic aucell \
  --expression data.h5ad \
  --regulons grn.parquet \
  --output auc.parquet

End-To-End Example¶

The repository includes a PBMC-3k example:

pip install "rustscenic[examples]"
python examples/pbmc3k_end_to_end.py

For collaborators or external testers, use the tester quickstart in the repository.