scTFBridge: a disentangled deep generative model informed by TF-motif binding for gene regulation inference in single-cell multi-omics

Feng-ao Wang, Chenxin Yi, Jiajun Chen, Ruikun He (), Junwei Liu () and Yixue Li ()
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Feng-ao Wang: University of Chinese Academy of Sciences
Chenxin Yi: Guangzhou National Laboratory
Jiajun Chen: Guangzhou National Laboratory
Ruikun He: BYHEALTH Institute of Nutrition & Health
Junwei Liu: Guangzhou National Laboratory
Yixue Li: University of Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract The interplay between transcription factors (TFs) and regulatory elements (REs) drives gene transcription, forming gene regulatory networks (GRNs). Advances in single-cell technologies now enable simultaneous measurement of RNA expression and chromatin accessibility, offering unprecedented opportunities for GRN inference at single-cell resolution. However, heterogeneity across omics layers complicates regulatory feature extraction. We present scTFBridge, a multi-omics deep generative model for GRN inference. scTFBridge disentangles latent spaces into shared and specific components across omics layers. By integrating TF-motif binding knowledge, scTFBridge aligns shared embeddings with specific TF regulatory activities, enhancing biological interpretability. Using explainability methods, scTFBridge computes regulatory scores for REs and TFs, enabling robust GRN inference. Our results further demonstrate that scTFBridge can identify cell-type-specific susceptibility genes and distinct regulatory programs, providing insights into gene regulation mechanisms at the single-cell level.

Date: 2025
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DOI: 10.1038/s41467-025-64227-y

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