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

Haiyi Mao, Minxue Jia, Marissa Di, Eleanor Valenzi, Xiaoyu Tracy Cai, Robert Lafyatis, Kun Zhang and Panayiotis V. Benos ()
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Haiyi Mao: University of Pittsburgh
Minxue Jia: University of Pittsburgh
Marissa Di: University of Pittsburgh
Eleanor Valenzi: University of Pittsburgh
Xiaoyu Tracy Cai: Loyola University Chicago
Robert Lafyatis: University of Pittsburgh
Kun Zhang: Carnegie Mellon University
Panayiotis V. Benos: University of Pittsburgh

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-63921-1

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