Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model.

ABSTRACT: A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. E

SUBMITTER: Gao H 

PROVIDER: S-EPMC11319358 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature