Identifying Combinatorial Regulatory Genes for Cell Fate Decision via Reparameterizable Subset Explanations

Publication Date: 8/3/2025

Event: 31st ACM SIGKDD Conference on Knowledge Discover and Data Mining (ACM KDD 2025)

Reference: pp. 1823-1832, 2025

Authors: Junhao Liu, University of California; Pengpeng Zhang, University of California; Martin Renqiang Min, NEC Laboratories America, Inc.; Jing Zhang, University of California

Abstract: Cell fate decisions are highly coordinated processes governed by complex interactions among numerous regulatory genes, while disruptions in these mechanisms can lead to developmental abnormalities and disease. Traditional methods often fail to capture such combinatorial interactions, limiting their ability to fully model cellfate dynamics. Here, we introduce MetaVelo, a global feature explanation framework for identifying key regulatory gene sets influencing cell fate transitions. MetaVelo models these transitions as a black-box function and employs a differentiable neural ordinary differential equation (ODE) surrogate to enable efficient optimization. By reparameterizing the problem as a controllable data generation process, MetaVelo overcomes the challenges posed by the non-differentiable nature of cell fate dynamics. Benchmarking across diverse stand-alone and longitudinal single-cell RNA-seq datasets and three black-box cell fate models demonstrates its superiority over 12 baseline methods in predicting developmental trajectories and identifying combinatorial regulatory gene sets. MetaVelo further distinguishes independent from synergistic regulatory genes, offering novel insights into the gene interactions governing cellfate. With the growing availability of high-resolution single-celldata, MetaVelo provides a scalable and effective framework.

Publication Link: https://dl.acm.org/doi/10.1145/3711896.3737000