Description
arboreto is an agent skill for inferring candidate gene regulatory networks from bulk or single-cell expression data, using the Arboreto library's GRNBoost2 and GENIE3 methods. GRNBoost2 uses stochastic gradient boosting regressions, and GENIE3 uses random forests. Each target gene is predicted from candidate regulators, excluding itself, to rank regulator-target associations. The skill is clear about what these results mean: they are observational predictive associations, not proof of direct binding, activation, repression or causal regulation. It also warns about the library's state. The latest PyPI release it checked was 0.1.6 from February 2021, and the Read the Docs site labels its docs 0.1.5, while the current GitHub source contains fixes that are not in the PyPI wheel. So an unpinned installation may not run inference, and the skill lists an exact stack that passed its smoke tests on macOS arm64 with Python 3.11. A reference file covers distributed computing. Use it when you need candidate regulators for a set of genes from expression data. It is part of the k-dense-ai scientific agent skills repository on GitHub, and the listing shows it as free.…
Highlights
- Ranks candidate regulator-target associations
- GRNBoost2 and GENIE3 methods
- Clear that results are not causal proof
- Warns about the PyPI release age and a pinned stack
- Reference for distributed computing
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arboreto FAQ
What does it infer?
Candidate gene regulatory networks, by ranking regulator-target associations from bulk or single-cell expression data.
Are the results causal?
No. The skill says they are observational predictive associations, not proof of direct binding, activation, repression or causal regulation.
Which methods does it use?
GRNBoost2, which uses stochastic gradient boosting regressions, and GENIE3, which uses random forests.
Will a plain install work?
Not guaranteed. The skill says the latest PyPI release it checked is from 2021 and an unpinned install may not run inference, so it lists a tested stack.
How do I install it?
Run npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill arboreto.
Is it checked for security?
Its security audits show passes from Gen Agent Trust Hub, Socket and Snyk.
What are the best arboreto alternatives?
Popular alternatives to arboreto include anndata, scanpy and scvelo. See all arboreto alternatives