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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3).
Passed 20 of 22 test cases with the skill, and 17 of 22 without it.
and 3 more in the full report
3 fixed18 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
My gene regulatory network calculations in Arboreto are running extremely slowly and occasionally returning empty results or Dask execution errors. Can you provide a practical troubleshooting guide detailing the main causes and how to fix these computational and data format issues?
| Checked | Without skill | With skill |
|---|---|---|
| The guide states that wrapping script execution in an if __name__ == '__main__': guard resolves Dask execution errors. | Missed | Passed |
| The guide recommends orienting expression data with genes as columns to avoid empty results. | Passed | Passed |
| The guide recommends using GRNBoost2 instead of GENIE3 to fix slow performance. | Missed | Passed |
The instructions your model reads when the skill loads.
--|-------------|
| TF | Transcription factor (regulator) |
| target | Target gene |
| importance | Regulatory importance score (higher = stronger) |
Filtering strategy:
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)
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 = []
test cases passed, vs 17 / 22 without
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