{"doi":"10.1093/nar/gkaf138","title":"Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data","abstract":"Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.","journal":"Nucleic Acids Research","year":2025,"id":509302,"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":38,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7735,"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":1020249,"name":"Changcheng Lu","orcid":"0000-0001-9263-8463","position":1,"is_corresponding":false},{"id":1363211,"name":"Shuting Jin","orcid":"0000-0002-8113-9367","position":2,"is_corresponding":false},{"id":1020248,"name":"Yajie Meng","orcid":"0000-0002-2384-1158","position":3,"is_corresponding":false},{"id":1363212,"name":"Xiangzheng Fu","orcid":"0000-0001-6840-2573","position":4,"is_corresponding":false},{"id":231859,"name":"Xiangxiang Zeng","orcid":"0000-0003-1081-7658","position":5,"is_corresponding":false},{"id":70301,"name":"Ruth Nussinov","orcid":"0000-0002-8115-6415","position":6,"is_corresponding":false},{"id":69831,"name":"Feixiong Cheng","orcid":"0000-0002-1736-2847","position":7,"is_corresponding":false},{"id":1020247,"name":"Junlin Xu","orcid":"0000-0003-1057-1504","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":null,"created_at":"2026-07-19T02:47:24.513904Z","pmid":"40037709","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":[]}