Interpretable models for scRNA-seq data embedding with multi-scale structure preservation

Interpretable models for scRNA-seq data embedding with multi-scale structure preservation | bioRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Interpretable models for scRNA-seq data embedding with multi-scale structure preservation View ORCID Profile David Novak , View ORCID Profile Cyril de Bodt , View ORCID Profile Pierre Lambert , View ORCID Profile John Aldo Lee , View ORCID Profile Sofie Van Gassen , View ORCID Profile Yvan Saeys doi: https://doi.org/10.1101/2023.11.23.568428 David Novak 1 Department of Mathematics, Computer Science and Statistics, Ghent University , Ghent, Belgium 2 Data Mining and Modeling for Biomedicine, Center for Inflammation Research , VIB-UGent, Ghent, Belgium 3 Division of Medical Oncology, Department of Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David Novak For correspondence: davidnovak9000{at}gmail.com Cyril de Bodt 4 Mathematics Department, Université de Namur , Namur, Belgium 5 Namur Institute for Complex Systems (naXys), Université de Namur , Namur, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cyril de Bodt Pierre Lambert 6 ICTEAM/ELEN, Université catholique de Louvain , Louvain-la-Neuve, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pierre Lambert John Aldo Lee 7 IREC/MIRO, Université catholique de Louvain , Brussesls, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John Aldo Lee Sofie Van Gassen 1 Department of Mathematics, Computer Science and Statistics, Ghent University , Ghent, Belgium 2 Data Mining and Modeling for Biomedicine, Center for Inflammation Research , VIB-UGent, Ghent, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sofie Van Gassen Yvan Saeys 1 Department of Mathematics, Computer Science and Statistics, Ghent University , Ghent, Belgium 2 Data Mining and Modeling for Biomedicine, Center for Inflammation Research , VIB-UGent, Ghent, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yvan Saeys Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF SUMMARY The ability to explore high-dimensional single-cell transcriptomics data efficiently is crucial in many biological studies. Dimensionality reduction techniques have therefore emerged as a basic building block of analytical workflows. They generate low-dimensional embeddings that capture important structures in the data, and are often used in discovery, quality control, and downstream analysis. However, the trustworthiness of current methods and the rigour of popular evaluation criteria are limited. We tackle this in an empirical study of structure-preserving data embeddings, delivering two tools. First, we introduce ViScore : a robust scoring framework that improves both unsupervised and supervised quality metrics, with emphasis on scalability and fairness. Second, we introduce ViVAE : a deep learning model that achieves better multi-scale structure preservation and is equipped with new tools for interpretability. We demonstrate the potential of these contributions to advance the trustworthiness of single-cell dimensionality reduction in a quantitative comparison and focused case studies. Competing Interest Statement The authors have declared no competing interest. Footnotes Revised structure and format to conform to Cell Systems guidelines Incorporated context about notions of local and global in topology, graph theory https://github.com/saeyslab/ViVAE https://github.com/saeyslab/ViScore Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Back to top Previous Next Posted April 17, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Interpretable models for scRNA-seq data embedding with multi-scale structure preservation David Novak , Cyril de Bodt , Pierre Lambert , John Aldo Lee , Sofie Van Gassen , Yvan Saeys bioRxiv 2023.11.23.568428; doi: https://doi.org/10.1101/2023.11.23.568428 Share This Article: Copy Citation Tools Interpretable models for scRNA-seq data embedding with multi-scale structure preservation David Novak , Cyril de Bodt , Pierre Lambert , John Aldo Lee , Sofie Van Gassen , Yvan Saeys bioRxiv 2023.11.23.568428; doi: https://doi.org/10.1101/2023.11.23.568428 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioinformatics Subject Areas All Articles Animal Behavior and Cognition (8164) Biochemistry (19027) Bioengineering (15171) Bioinformatics (45807) Biophysics (22958) Cancer Biology (20248) Cell Biology (27495) Clinical Trials (138) Developmental Biology (14233) Ecology (21529) Epidemiology (2067) Evolutionary Biology (25940) Genetics (16436) Genomics (23979) Immunology (19175) Microbiology (43486) Molecular Biology (18481) Neuroscience (95839) Paleontology (717) Pathology (3064) Pharmacology and Toxicology (5253) Physiology (8339) Plant Biology (16378) Scientific Communication and Education (2123) Synthetic Biology (4675) Systems Biology (10450) Zoology (2436)

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