arboreto

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name: arboreto description: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. license: BSD-3-Clause license tags: [scientific-skills, arboreto, bioinformatics, compliance] metadata: skill-author: K-Dense Inc. -----|-------------| | 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:

# 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:

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

Run multiple seeds for robustness analysis:

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