Interpretable representation learning for 3D multi-piece intracellular structures using point clouds.

ABSTRACT: A key challenge in understanding subcellular organization is quantifying interpretable measurements of intracellular structures with complex multi-piece morphologies in an objective, robust and generalizable manner. Here we introduce a morphology-appropriate representation learning framework that uses 3D rotation invariant autoencoders and point clouds. This framework is used to learn representations of complex multi-piece morphologies that are independent of orientation, compact, and easy to interpret. We apply our framework to intracellular structures with punctate morphologies (e.g. DNA replication foci) and polymorphic morphologies (e.g. nucleoli). We systematically compare our framework to image-based autoencoders across several intracellular structure datasets, including a synthetic

SUBMITTER: Vasan R 

PROVIDER: S-EPMC11291148 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature