Multi-omic relay velocity modeling uncovers dynamic chromatin-transcription regulation across cell states.

ABSTRACT: Cell fate transitions emerge from dynamic gene expression programs, yet existing RNA velocity models primarily rely on RNA abundance and globally inferred latent time, limiting their ability to capture local regulatory dynamics. To address these limitations, we introduce MoFlow, a deep neural network that integrates multi-omic data within a relay velocity framework. Unlike previous approaches, MoFlow flexibly infers velocity parameters at single-cell resolution without pre-assigned latent time, enabling a comprehensive and locally adaptive estimation of gene expression kinetics. Applied to single cell multi-omic datasets from brain, skin, and blood cells, MoFlow distinguished chromatin-dependent and independent transcriptional regulation, validated transcription repression models, and iden

SUBMITTER: Hong A 

PROVIDER: S-EPMC12808240 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature