Experimental design & power analysis for perturbation screens

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.

Start by reviewing the proposed pertpy.tools design module and the existing Distance/DistanceTest, prediction, and perturbation-space APIs. Scope the power-calculation and active-learning pieces before implementation, since the issue describes several greenfield capabilities. Done should be a clearly defined design and implementation for the selected scope, including simulation-based evaluation where applicable.