{"doi":"10.3390/genes15010054","title":"A Comprehensive Evaluation of Generalizability of Deep Learning-Based Hi-C Resolution Improvement Methods","abstract":"Hi-C is a widely used technique to study the 3D organization of the genome. Due to its high sequencing cost, most of the generated datasets are of a coarse resolution, which makes it impractical to study finer chromatin features such as Topologically Associating Domains (TADs) and chromatin loops. Multiple deep learning-based methods have recently been proposed to increase the resolution of these datasets by imputing Hi-C reads (typically called upscaling). However, the existing works evaluate these methods on either synthetically downsampled datasets, or a small subset of experimentally generated sparse Hi-C datasets, making it hard to establish their generalizability in the real-world use case. We present our framework-Hi-CY-that compares existing Hi-C resolution upscaling methods on seven experimentally generated low-resolution Hi-C datasets belonging to various levels of read sparsities originating from three cell lines on a comprehensive set of evaluation metrics. Hi-CY also includes four downstream analysis tasks, such as TAD and chromatin loops recall, to provide a thorough report on the generalizability of these methods. We observe that existing deep learning methods fail to generalize to experimentally generated sparse Hi-C datasets, showing a performance reduction of up to 57%. As a potential solution, we find that retraining deep learning-based methods with experimentally generated Hi-C datasets improves performance by up to 31%. More importantly, Hi-CY shows that even with retraining, the existing deep learning-based methods struggle to recover biological features such as chromatin loops and TADs when provided with sparse Hi-C datasets. Our study, through the Hi-CY framework, highlights the need for rigorous evaluation in the future. We identify specific avenues for improvements in the current deep learning-based Hi-C upscaling methods, including but not limited to using experimentally generated datasets for training.","journal":"Genes","year":2023,"id":367799,"datarank":0.31195935653121476,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.043195436147006515,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.043195436147006515,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":5,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9514,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1125402,"name":"Atishay Jain","orcid":"0000-0001-9972-4217","position":1,"is_corresponding":false},{"id":1125801,"name":"Madeline Hughes","orcid":null,"position":2,"is_corresponding":false},{"id":280975,"name":"Justin Wagner","orcid":"0009-0003-8903-0504","position":3,"is_corresponding":false},{"id":347248,"name":"Ritambhara Singh","orcid":"0000-0002-7523-160X","position":4,"is_corresponding":false},{"id":997548,"name":"Ghulam Murtaza","orcid":"0000-0002-1803-1134","position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:15:16.306414Z","pmid":"38254945","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":[]}