Longitudinal modality prediction learns gene regulatory patterns: insights from a single-cell competition

Abstract

Simultaneous measurement of chromatin, transcriptomic, and proteomic features in single cells opens new avenues for modeling interactions between molecular layers during dynamic biological processes. Predicting one modality from another - such as inferring gene expression from chromatin profiles or protein abundance from RNA - has the potential to reveal regulatory relationships and enhance downstream analyses. However, conventional approaches for predicting gene regulation have largely failed and method development in modality prediction for regulatory inference has been limited. To explore effective modeling strategies and stimulate innovation, we generated a purpose-built longitudinal multimodal benchmarking dataset that captures early hematopoietic differentiation and organized the largest single-cell data competition to date, receiving over 27,000 submissions from 1,602 competitors worldwide. In our extensive analysis of the competition results, we demonstrated that top-performing approaches outperform state-of-the-art methods, and uncover how best-performing models captured biologically meaningful regulatory relationships between modalities. With ablation studies of the winning models, we identified feature-engineering strategies, model architectures and cross-validation schemes that are crucial for outstanding performance, and provide simplified, reproducible, light-weight code for state-of-the-art models. Together, the benchmark and analyses serve as an evaluation standard and guide future method development, including recently emerging foundation models, to advance our understanding of regulatory interactions in longitudinal, multimodal single-cell data.