PerturbPlan: An analytical framework for designing Perturb-seq experiments.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• CRISPR screens coupled with single-cell RNA sequencing (scRNA-seq) are vital for understanding gene and noncoding element functions.
• Designing these complex experiments requires balancing statistical power and experimental costs, a challenge due to numerous parameters.

Purpose of the Study:

• To develop a computationally efficient method for power calculation in single-cell CRISPR screens.
• To create an interactive tool that facilitates optimal experimental design for Perturb-seq and targeted Perturb-seq (TAP-seq).

Main Methods:

• Derived a novel analytical formula for calculating the statistical power of detecting perturbation-expression associations.
• Developed PerturbPlan, a web application implementing the analytical formula for interactive experimental design.
• Applied PerturbPlan to analyze and compare different single-cell CRISPR screen designs.

Main Results:

• The analytical formula reduces runtime for power calculations by up to seven orders of magnitude compared to simulation-based methods.
• PerturbPlan successfully guided comparative analyses of existing Perturb-seq designs and quantified cost-effectiveness of TAP-seq.
• Demonstrated how optimal experimental design choices vary with scale and readout type.

Conclusions:

• PerturbPlan offers the first flexible and interactive solution for designing well-powered single-cell CRISPR screens.
• The tool aids researchers in making informed decisions regarding experimental scale, design, and readout strategies (e.g., TAP-seq vs. Perturb-seq).
• Accelerated power calculations enable more accessible and efficient experimental planning in functional genomics.