Context
Before spending significant money on a Perturb-seq or drug screen, users need to plan it, and afterwards justify that it was adequately powered.
pertpy has no tooling for this today, and little exists anywhere — largely greenfield.
What's missing
- A priori power / sample-size calculation: given an expected effect size (e.g. in E-distance / logFC terms) and variability, how many cells per perturbation and how many replicates are needed to detect it at a target power?
- Post-hoc power / minimal detectable effect from a pilot dataset.
- Perturbation selection / active learning: with a prediction model, which perturbations to include in the next round of an iterative screen to maximize information.
Proposal / API
A pertpy.tools design module.
Power calculations can be simulation-based, reusing Distance / DistanceTest to define effect sizes consistent with the rest of pertpy.
Active-learning selection builds on the prediction + perturbation-space APIs.
Why it matters
- Industry: directly reduces wasted screening spend and supports go/no-go decisions.
- Academia: reviewers increasingly ask for power justification, and iterative screen design is an active research area.
Context
Before spending significant money on a Perturb-seq or drug screen, users need to plan it, and afterwards justify that it was adequately powered.
pertpy has no tooling for this today, and little exists anywhere — largely greenfield.
What's missing
Proposal / API
A
pertpy.toolsdesign module.Power calculations can be simulation-based, reusing
Distance/DistanceTestto define effect sizes consistent with the rest of pertpy.Active-learning selection builds on the prediction + perturbation-space APIs.
Why it matters