{"id":438,"title":"scSTEC: Annotating Chromatin Subcompartments from Individual Single-Cell Hi-C Data via Direct Two-Dimensional Embedding and Chromosome-wise Consensus","title_zh":"scSTEC：基于二维嵌入与染色体共识从单细胞Hi-C数据注释染色质亚区室","url":"https://doi.org/10.64898/2026.10.28.762654","doi":"10.64898/2026.10.28.762654","pmid":null,"journal":"bioRxiv","kind":"preprint","lang":"en","published_at":"2026-11-03T00:00:00+00:00","fetched_at":"2026-11-08T03:44:14+00:00","authors":["Peng Y","Yang L","Ge H"],"primary_topic":"ethics-policy","topics":[{"slug":"omics-methods","score":8.0,"hits":["hi-c","single-cell"]},{"slug":"ethics-policy","score":4.0,"hits":["consensus"]},{"slug":"ai-compute","score":1.0,"hits":["deep learning"]},{"slug":"animal-models","score":0.8,"hits":["mouse embryos"]}],"topic_score":8.0,"preference_score":0.0,"preference_labels":[],"llm_score":69.0,"final_score":69.0,"score_dims":{"novelty":85,"rigor":72,"impact":66,"relevance":48,"translation":42},"recommend_reason":"方法新颖、算法解释性较好，对胚胎与发育单细胞3D基因组研究有参考价值；但属预印本且与ART临床决策距离较远，生殖临床读者可浏览方法学部分。","keywords":["单细胞Hi-C","染色质亚区室","三维基因组","二维嵌入","胚胎发育","计算方法"],"selected":1,"story_id":124,"llm_provider":"deepseek","llm_model":"deepseek-flash","llm_effort":"low","source_key":"europepmc-preprints","source_name":"Europe PMC · 预印本（领域内）","source_tier":1,"mention_count":1,"has_interpretation":false,"journal_key":"biorxiv","journal_tier":"standard","journal_if":null,"is_mock":false,"summary_zh":"该预印本提出scSTEC，用于直接从单个细胞的scHi-C接触矩阵注释染色质亚区室，无需数据填补、复杂上游流程或跨细胞信息共享。作者发现scGHOST构建的跨染色体矩阵本质为秩2，可利用二维基因组嵌入与染色体间共识完成注释。在bulk Hi-C、降采样模拟及人类细胞系、小鼠皮层、发育胚胎和嗅觉感觉神经元等真实scHi-C数据中，scSTEC注释效果与scGHOST相当且更易解释，并能区分细胞类型、捕捉发育动态及关联基因表达与表观遗传谱。","summary":"Motivation Single-cell Hi-C (scHi-C) technologies enable the study of three-dimensional (3D) genome organization at individual-cell resolution. Many computational methods have been developed to infer hierarchical structures from scHi-C data. However, annotating chromatin subcompartments from sparse scHi-C data remains a challenge. The recently developed scGHOST provides a framework for single-cell subcompartment annotation, but relies on a deep learning-based architecture, a multi-step upstream pipeline involving cell embedding and matrix imputation, and cross-cell information sharing. These features complicate algorithmic interpretation and make the annotation of an individual cell dependent on information from other cells in the dataset. Hence, methods are needed that annotate subcompartments directly from an individual cell’s own contact matrix, without complex upstream processing or cross-cell information sharing. Results We developed scSTEC, a computational framework that annotates subcompartments directly from an individual scHi-C matrix without data imputation, complex upstream processing, or cross-cell data sharing. We show that the estimated inter-chromosomal matrix constructed by scGHOST is intrinsically of rank 2, revealing that the geometry relevant to clustering can be represented in two dimensions. Leveraging this low-dimensional structure, scSTEC performs direct subcompartment annotation through 2D genomic embedding and chromosome-wise consensus. Across bulk Hi-C, downsampled simulations, and real scHi-C datasets (four human cell lines, adult mouse cortex, developing mouse embryos, and developing olfactory sensory neurons), scSTEC achieves subcompartment annotations comparable to scGHOST despite operating on each cell independently, while providing greater algorithmic interpretability. Furthermore, subcompartments identified by scSTEC effectively resolve cell types, capture developmental dynamics, and show consistent associations with gene expression and epigenetic profiles. Availability scSTEC is freely available at https://github.com/Ge-lab-pku/scSTEC.","interpretation_basis":null,"fulltext_status":null,"interpretation":null,"fulltext":{"available":false,"status":null,"reason":"尚未抓取全文（未入选、分数未达阈值，或还没跑 fulltext 步骤）","pmcid":null,"license":null,"chars":0,"source":null,"fetched_at":null}}