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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3).
Passed 22 of 23 test cases with the skill, and 18 of 23 without it.
and 3 more in the full report
5 fixed17 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write the shell command to execute Arboreto's included `scripts/basic_grn_inference.py` script on input matrix `counts.tsv`, saving output to `network.tsv`, restricting regulators to `tfs.txt`, setting seed to 100, and setting link limit to 2000.
| Checked | Without skill | With skill |
|---|---|---|
| Uses `--tf-file tfs.txt` to specify the transcription factors. | Missed | Passed |
| Uses `--limit 2000` to limit the output edges. | Passed | Passed |
The instructions your model reads when the skill loads.
Use Arboreto to rank candidate regulator-target associations from expression measurements. GRNBoost2 fits stochastic gradient boosting regressions; GENIE3 fits random forests. Each target is predicted from candidate regulators, excluding itself. These are observational predictive associations, not proof of direct binding, activation/repression, or causal regulation.
The latest PyPI release checked is 0.1.6 (2021-02-09). Read the Docs still labels its documentation 0.1.5; the current GitHub source contains fixes that are not in the PyPI wheel. Do not assume a successful unpinned installation can run inference. See compatibility and distribution details.
The following exact stack passed dense and CSC-sparse GRNBoost2, dense GENIE3, custom GBM/RF, and wrapper smoke tests on macOS arm64 with Python 3.11.11:
uv venv --python 3.11 .venv-arboreto
uv pip install --python .venv-arboreto/bin/python \
'arboreto==0.1.6' 'dask[complete]==2024.7.1' 'distributed==2024.7.1' \
'numpy==1.26.4' 'pandas==2.2.3' 'scikit-learn==1.5.2' 'scipy==1.13.1'
This is a bounded compatibility recipe, not a claim that current releases of all
dependencies work. PyPI Arboreto builds an empty metadata graph that the newer
Dask dataframe implementation rejects. For this pinned Dask version, select its
legacy dataframe backend before importing Arboreto or dask.dataframe:
import dask
dask.config.set({"dataframe.query-planning": False})
from arboreto.algo import grnboost2, genie3
The bundled wrapper does this for Dask 2024.7.1. Restart an existing notebook kernel if it has already imported the newer dataframe backend. Sparse targets
test cases passed, vs 18 / 23 without
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