ScACT: Accurate Cross-modality Translation via Cycle-consistent Training from Unpaired Single-cell Data.

ABSTRACT: Single-cell sequencing technologies have revolutionized genomics by enabling the simultaneous profiling of various molecular modalities within individual cells. Their integration, especially cross-modality translation, offers deep insights into cellular regulatory mechanisms. Many methods have been developed for cross-modality translation, but their reliance on scarce high-quality co-assay data limits their applicability. Addressing this, we introduce scACT, a deep generative model designed to extract cross-modality biological insights from unpaired single-cell data. scACT tackles three major challenges: aligning unpaired multi-modal data via adversarial training, facilitating cross-modality translation without prior knowledge via cycle-consistent training, and enabling interpretable regul

SUBMITTER: Xu S 

PROVIDER: S-EPMC11611688 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature