Loading...
HALO: hierarchical causal modeling for single cell multi-omics data
Mao, Haiyi Jia, Minxue Di, Marissa Valenzi, Eleanor Cai, Xiaoyu Tracy Lafyatis, Robert Zhang, Kun Benos, Panayiotis V.
Mao, Haiyi
Jia, Minxue
Di, Marissa
Valenzi, Eleanor
Cai, Xiaoyu Tracy
Lafyatis, Robert
Zhang, Kun
Benos, Panayiotis V.
Files
Loading...
s41467-025-63921-1.pdf
Adobe PDF, 7.62 MB
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
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 decoupled latent representations, revealing their dynamic interplay. On the individual gene level, HALO matches gene-peak pairs and characterizes their changes over time. HALO discovers analogous biological functions between modalities, distinguishes epigenetic factors for lineage specification, and identifies temporal cis-regulation interactions relevant to cellular differentiation and human diseases.
Citation
H. Mao et al., “HALO: hierarchical causal modeling for single cell multi-omics data,” Nature Communications 2025 16:1, vol. 16, no. 1, pp. 8892-, Oct. 2025, doi: 10.1038/s41467-025-63921-1
Source
Nature Communications
Conference
Keywords
Computational Models, Gene Regulatory Networks, Machine Learning, Software
Subjects
Source
Publisher
Springer Nature