Toward 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, interspecies 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 can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to th

SUBMITTER: Rosen Y 

PROVIDER: S-EPMC11310084 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature