Design-of-Experiments for Nonlinear, Multivariate Biology: Rethinking Experimental Design through Perturb-seq

Abstract

Design of experiments (DOE) principles are increasingly applied to biological assays, yet it remains unclear whether the optimality of their foundational assumption--orthogonal decomposition--holds in nonlinear biological systems. We addressed this question using Perturb-seq as a case study. By benchmarking a design commonly used in Perturb-seq and related experiments against the orthogonal Plackett-Burman (PB) design via simulations, we uncovered a counter-intuitive phenomenon: while orthogonal designs generally excel, the correlated structure inherent to the common design is functionally robust in systems with significant signal amplification. This challenges the blind application of DOE to biology. Based on these findings, we developed the PB suitability index (PBSI), a simple, parameter-free metric that predicts the optimal design solely from network structure. Our work not only provides practical guidelines for Perturb-seq but also establishes a "biology-oriented DOE" framework, bridging the gap between statistical rigor and biological complexity.