mkurman/arboreto

--|-------------| | TF | Transcription factor (regulator) | | target | Target gene | | importance | Regulatory importance score (higher = stronger) |

Filtering strategy:

• Top N links per target gene
• Importance threshold (e.g., > 0.5)
• Statistical significance testing (permutation tests)

Integration with pySCENIC

Arboreto is a core component of the SCENIC pipeline for single-cell regulatory network analysis:

python
# Step 1: Use arboreto for GRN inference
from arboreto.algo import grnboost2
network = grnboost2(expression_data=sc_data, tf_names=tf_list)

# Step 2: Use pySCENIC for regulon identification and activity scoring
# (See pySCENIC documentation for downstream analysis)

Reproducibility

Always set a seed for reproducible results:

python
network = grnboost2(expression_data=matrix, seed=777)

Run multiple seeds for robustness analysis:

python
from distributed import LocalCluster, Client

if __name__ == '__main__':
    client = Client(LocalCluster())

    seeds = [42, 123, 777]
    networks = []

    for seed in seeds:
        net = grnboost2(expression_data=matrix, client_or_address=client, seed=seed)
        networks.append(net)

    # Combine networks and filter consensus links
    consensus = analyze_consensus(networks)

Troubleshooting

Memory errors: Reduce dataset size by filtering low-variance genes or use distributed computing

Slow performance: Use GRNBoost2 instead of GENIE3, enable distributed client, filter TF list

Dask errors: Ensure if __name__ == '__main__': guard is present in scripts

Empty results: Check data format (genes as columns), verify TF names match gene names