{"doi":"10.1093/bioinformatics/btaf575","title":"SHICEDO: single-cell Hi-C data enhancement with reduced over-smoothing","abstract":"MOTIVATION: Single-cell Hi-C (scHi-C) technologies have significantly advanced our understanding of the 3D genome organization. However, scHi-C data are often sparse and noisy, leading to substantial computational challenges in downstream analyses. RESULTS: In this study, we introduce SHICEDO, a novel deep-learning model specifically designed to enhance scHi-C contact matrices by imputing missing or sparsely captured chromatin contacts through a generative adversarial framework. SHICEDO leverages the unique structural characteristics of scHi-C matrices to derive customized features that enable effective data enhancement. Additionally, the model incorporates a channel-wise attention mechanism to mitigate the over-smoothing issue commonly associated with scHi-C enhancement methods. Through simulations and real-data applications, we demonstrate that SHICEDO outperforms the state-of-the-art methods, achieving superior quantitative and qualitative results. Moreover, SHICEDO enhances key structural features in scHi-C data, thus enabling more precise delineation of chromatin structures such as A/B compartments, TAD-like domains, and chromatin loops. AVAILABILITY AND IMPLEMENTATION: SHICEDO is publicly available at https://github.com/wmalab/SHICEDO.","journal":"Bioinformatics","year":2025,"id":578940,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9498,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1488798,"name":"Rui Ma","orcid":"0000-0003-0512-8751","position":1,"is_corresponding":false},{"id":1161464,"name":"Michael Strobel","orcid":"0009-0000-3829-0048","position":2,"is_corresponding":false},{"id":207967,"name":"Yangyang Hu","orcid":null,"position":3,"is_corresponding":false},{"id":1488799,"name":"Tiantian Ye","orcid":"0009-0000-7262-8970","position":4,"is_corresponding":false},{"id":397443,"name":"Tao Jiang","orcid":"0000-0003-3833-4498","position":5,"is_corresponding":false},{"id":891407,"name":"Wenxiu Ma","orcid":"0000-0003-4097-1621","position":6,"is_corresponding":false},{"id":1298837,"name":"Jingong Huang","orcid":"0000-0001-6670-8093","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":null,"created_at":"2026-07-19T02:58:24.957414Z","pmid":"41129292","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}