COTree: A Statistical Framework for Deciphering Cell-Resolved Multi-Omics Trajectories
Recent advances in whole-cell modeling enable the computational tracking of the temporal evolution of thousands of molecular species across genomic, transcriptomic, proteomic, and metabolomic layers. These models provide a complementary perspective for studying cellular dynamics, offering continuous, system-wide observations that are difficult to obtain from experimental technologies, which are often destructive and yield only static measurements from limited modalities. While whole-cell models generate multi-omic simulation trajectories with high temporal resolution, analyzing and interpreting such complex data remains a major challenge that limits their potential to elucidate cellular dynamics. To address this challenge, we propose COTree, a statistical framework that learns integrated multi-omic representations and constructs a trajectory principal tree to summarize cellular progression patterns. COTree enables a broad range of downstream analyses, including cell classification, fate prediction, developmental time detection, and driver species identification, that provide new insights into how cells develop and differentiate. To demonstrate its practical utility, we apply COTree to a multi-omic trajectory dataset generated from the whole-cell model of JCVI-Syn3A, revealing cell types, characterizing long-term cellular dynamics, and identifying key driver species associated with cell death and replication.