Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN.

Autor: Rosen Y; Department of Computer Science, Stanford University, Stanford, CA, USA., Brbić M; School of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland., Roohani Y; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA., Swanson K; Department of Computer Science, Stanford University, Stanford, CA, USA., Li Z; Department of Computer Science and Technology, Tsinghua University, Beijing, China., Leskovec J; Department of Computer Science, Stanford University, Stanford, CA, USA. jure@cs.stanford.edu.
Jazyk: angličtina
Zdroj: Nature methods [Nat Methods] 2024 Aug; Vol. 21 (8), pp. 1492-1500. Date of Electronic Publication: 2024 Feb 16.
DOI: 10.1038/s41592-024-02191-z
Abstrakt: 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 three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.
(© 2024. The Author(s).)
Databáze: MEDLINE
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