scTFBridge: A Disentangled Deep Generative Model Informed by TF-Motif Binding for Gene Regulation Inference in Single-Cell Multi-Omics

scTFBridge: A Disentangled Deep Generative Model Informed by TF-Motif Binding for Gene Regulation Inference in Single-Cell Multi-Omics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article scTFBridge: A Disentangled Deep Generative Model Informed by TF-Motif Binding for Gene Regulation Inference in Single-Cell Multi-Omics Junwei Liu, Feng-ao Wang, Ruikun He, Yixue Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6093896/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Oct, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version 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. It consistently outperformed baseline methods in both cis- and trans-regulation inference tasks. Our results demonstrate that scTFBridge can uncover cell-type-specific susceptibility genes and distinct regulatory programs, offering new insights into gene regulation mechanisms at single-cell resolution. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Gene regulatory networks Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementarytable.xlsx supplementary table supplementarydata.xlsx supplementary data supplementaryinformationsubmit.docx supplementary information Cite Share Download PDF Status: Published Journal Publication published 15 Oct, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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