Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN.

ABSTRACT: Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, inter-species genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here, we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes' biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN has a unique ability to detect functionally related genes co-expressed across species, redefining differential expression for cross-species analysis

SUBMITTER: Rosen Y 

PROVIDER: S-EPMC9915700 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature