bioRxivJClub
COTree: A Statistical Framework for Deciphering Cell-Resolved Multi-Omics Trajectories
bioRxiv · · Preprint · Open access
Yuan, Wei + 5 more
Abstract ↗AI summary
The abstract is read at the publisher; the summary is JClub's.
COTree summarizes JCVI-Syn3A whole-cell multi-omics simulations with a trajectory principal tree, identifying key driver species linked to cell death and replication.
- Why it matters: Whole-cell models can generate continuous, system-wide trajectories, but interpreting their high-dimensional multi-omic outputs is difficult, limiting insights into cellular progression.
- What they did: COTree learns integrated multi-omic representations and builds a trajectory principal tree, then supports analyses such as cell classification, fate prediction, developmental time detection, and driver species identification.
- The result: Applied to JCVI-Syn3A simulation trajectories, it reveals cell types, characterizes long-term dynamics, and identifies driver species associated with cell death and replication.
The findingWhy it mattersWhat they didThe result
AI summary of the abstract · may contain errors · not medical advice · read the paper.
- Open access