HALO: hierarchical causal modeling for single cell multi-omics data.

ABSTRACT: Though open chromatin may promote active transcription, gene expression responses may not be directly coordinated with changes in chromatin accessibility. Most existing methods for single-cell multi-omics data focus only on learning stationary, shared information among these modalities, overlooking modality-specific information delineating cellular states and dynamics resulting from causal relations among modalities. To address this, the epigenome-transcriptome relationship can be characterized in relation to time as coupled (changing dependently) or decoupled (changing independently). We propose the framework HALO, adopting a causal approach to model these temporal causal relations on two levels. On the representation level, HALO factorizes these two modalities into both coupled and decou

SUBMITTER: Mao H 

PROVIDER: S-EPMC12504611 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature