Year: 2025
Type: article
Abstract: Embedding is the key step in single-cell Hi-C (scHi-C) analysis which relies on capturing biological meaningful heterogeneity at various levels of genome architecture. To understand the strength and limitations of existing tools in various applications, here we use ten scHi-C datasets to benchmark thirteen embedding tools including Va3DE, a new convolutional neural network model that can accommodate large cell numbers. We built a software framework to decouple the preprocessing options of existing tools and found that no single tool works best across all datasets under default settings. The difficulty levels and preferred resolutions are different between benchmark datasets, and the choice of data representation and preprocessing strongly impact the embedding performance. Embedding cells from early embryonic stages relies on long-range compartment-scale contacts, but resolving cell cycle phases and complex tissue requires short-range loop-scale contacts. Both random-walk and inverse document frequency (IDF) transformation prefers long-range "compartment-scale" over short-range "loop-scale" embedding, while deep-learning methods better overcome sparsity at both scales and are more versatile with different resolutions. Finally, "diagonal integration" with independent data modal is a promising approach to distinguish similar cell subpopulations. Our findings underscore the significance of appropriate priors for scHi-C embedding and also offer insights into genome architecture heterogeneity.
Source: Nature Communications
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: National Science Foundation, U.S. Department of Health and Human Services, University of Miami, Case Western Reserve University, National Institutes of Health, National Institute on Aging, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke
Awards: G1099034210, 53BP1 regulates genome biology and cellular physiology through liquid phase separation, G1244842289, G1295824700, G1426676655, Computational Genomic Epidemiology of Cancer (CoGEC) Training Program, G3613899374, FET: CCF: Small: Computational Drug Prediction through Joint Learning, Molecular Dissection of Cytokine Crosstalk in the Tumor Microenvironment, G4330114199, G445699482, G4692453154, G4734386973, Functional genomic studies in diverse populations to characterize risk loci for Alzheimer Disease, G498672744, G6501870817, G7000015062, RAPID:Genomic Variation Analysis of Coronavirus to Better Understand the Spread of COVID-19, G782874642, G794412064, G8009838363, G8209644491, PIPP Phase I: Comprehensive, Integrated, Intelligent System for Early and Accurate Pandemic Prediction, Prevention, and Preparation at Personal and Population Levels, Mapping heritable chromatin loop variants with allele-specific Hi-C analysis, G9000068178, G9000203770, G9000219505, Case Comprehensive Cancer Center Support Grant, Robust mapping of chromatin loops from sparse or single cell Hi-C data with DeepLoop, Mechanisms of PIK3CA helical domain mutations driving colorectal tumorigenesis, STAG2 mutations and 3D genome organization in glioblastoma multiforme, Alzheimer's MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery, Targeting Oncostatin M-Receptor to Suppress Metastasis and Therapy Failure
OpenAlex ID: W4415149221