bio-gene-regulatory-networks-perturbation-simulation
Simulate transcription factor perturbation effects on cell state using CellOracle, which integrates GRN inference with in silico knockout and overexpression modeling. Predicts cell identity shifts and differentiation trajectory changes from TF perturbations. Use when predicting the effect of transcription factor knockouts, planning perturbation experiments, or identifying driver TFs for cell fate transitions.
npx skills add BioTender-max/awesome-bio-agent-skills --skill perturbation-simulation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: anndata 0.10+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Perturbation Simulation **"Predict what happens if I knock out this transcription factor"** → Simulate TF perturbation effects on cell identity by combining a base GRN from accessible chromatin with learned regulatory weights from scRNA-seq, then propagating the perturbation signal to predict cell state shifts. - Python: `celloracle.Oracle()` for GRN construction and perturbation simulation Simulate transcription factor perturbation effects on cell state using CellOracle. Integrates GRN inference from scRNA-seq with base GRN from chromatin accessibility to predict cell identity shifts from TF knockouts or overexpression. ## CellOracle Overview CellOracle constructs a GRN by com
- Version Compatibility
- CellOracle Overview
- Installation
- Step 1: Base GRN from Accessible Regions
- From scATAC-seq Peaks
- From Published Chromatin Data
- Step 2: GRN Construction from scRNA-seq
- Step 3: Perturbation Simulation
- Knockout Simulation
- Overexpression Simulation
- Multi-TF Perturbation
- Visualization
- Quiver Plot (Vector Field)
- Gradient Plot
pip install celloracle
What does the bio-gene-regulatory-networks-perturbation-simulation skill do?
Simulate transcription factor perturbation effects on cell state using CellOracle, which integrates GRN inference with in silico knockout and overexpression modeling. Predicts cell identity shifts and differentiation trajectory changes from TF perturbations. Use when predicting the effect of transcription factor knockouts, planning perturbation experiments, or identifying driver TFs for cell fate transitions.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill perturbation-simulation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.