{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T02:53:53Z","timestamp":1781578433192,"version":"3.54.5"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T00:00:00Z","timestamp":1710633600000},"content-version":"vor","delay-in-days":55,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020AAA0107100"],"award-info":[{"award-number":["2020AAA0107100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62325604"],"award-info":[{"award-number":["62325604"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276271"],"award-info":[{"award-number":["62276271"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https:\/\/github.com\/DayuHuu\/scEMC.<\/jats:p>","DOI":"10.1093\/bib\/bbae102","type":"journal-article","created":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T10:03:05Z","timestamp":1710669785000},"source":"Crossref","is-referenced-by-count":59,"title":["Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data"],"prefix":"10.1093","volume":"25","author":[{"given":"Dayu","family":"Hu","sequence":"first","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yawei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Medical Big Data Research Center, Chinese PLA General Hospital , No. 28 Fuxing Road, 100853 Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunlun","family":"He","sequence":"additional","affiliation":[{"name":"Medical Big Data Research Center, Chinese PLA General Hospital , No. 28 Fuxing Road, 100853 Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,3,16]]},"reference":[{"key":"2024031710030025800_ref1","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s41576-023-00580-2","article-title":"Methods and applications for single-cell and spatial multi-omics","volume":"24","author":"Vandereyken","year":"2023","journal-title":"Nat Rev Genet"},{"issue":"6","key":"2024031710030025800_ref2","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1111\/tpj.15772","article-title":"Advances and applications of single-cell omics technologies in plant research","volume":"110","author":"Mo","year":"2022","journal-title":"Plant J"},{"issue":"3","key":"2024031710030025800_ref3","doi-asserted-by":"crossref","first-page":"e694","DOI":"10.1002\/ctm2.694","article-title":"Single-cell RNA sequencing technologies and applications: a brief overview","volume":"12","author":"Jovic","year":"2022","journal-title":"Clin Transl Med"},{"key":"2024031710030025800_ref4","first-page":"bbad216","article-title":"scDFC: a deep fusion clustering method for single-cell RNA-seq data","author":"Dayu","year":"2023","journal-title":"Brief Bioinform"},{"issue":"5","key":"2024031710030025800_ref5","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/nmeth.4236","article-title":"SC3: consensus clustering of single-cell RNA-seq data","volume":"14","author":"Kiselev","year":"2017","journal-title":"Nat Methods"},{"issue":"1","key":"2024031710030025800_ref6","first-page":"100","article-title":"Algorithm as 136: a k-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J R Stat Soc Ser C Appl Stat"},{"key":"2024031710030025800_ref7","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","author":"Von Luxburg","year":"2007","journal-title":"Statistics and computing"},{"key":"2024031710030025800_ref8","doi-asserted-by":"crossref","DOI":"10.1093\/nargab\/lqaa039","article-title":"Deep soft K-means clustering with self-training for single-cell RNA sequence data","volume":"2","author":"Chen","year":"2020","journal-title":"NAR genomics and bioinformatics"},{"key":"2024031710030025800_ref9","article-title":"Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis","volume":"11","author":"Li","year":"2020","journal-title":"Nat Commun"},{"issue":"4","key":"2024031710030025800_ref10","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/s42256-019-0037-0","article-title":"Clustering single-cell RNA-seq data with a model-based deep learning approach","volume":"1","author":"Tian","year":"2019","journal-title":"Nat Mach Intell"},{"issue":"8","key":"2024031710030025800_ref11","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1093\/bioinformatics\/btac099","article-title":"scGAC: a graph attentional architecture for clustering single-cell RNA-seq data","volume":"38","author":"Cheng","year":"2022","journal-title":"Bioinformatics"},{"key":"2024031710030025800_ref12","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac018","article-title":"Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network","volume":"23","author":"Gan","year":"2022","journal-title":"Brief Bioinform"},{"issue":"9","key":"2024031710030025800_ref13","doi-asserted-by":"crossref","first-page":"100577","DOI":"10.1016\/j.patter.2022.100577","article-title":"Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer","volume":"3","author":"Amodio","year":"2022","journal-title":"Patterns"},{"key":"2024031710030025800_ref14","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/art.42683","article-title":"Multi-modal single cell sequencing of B cells in primary Sj\u00f6gren\u2019s syndrome","volume":"76","author":"Arvidsson","year":"2023","journal-title":"Arthritis Rheumatol"},{"key":"2024031710030025800_ref15","first-page":"1","article-title":"Integration of multi-modal single-cell data","author":"Lee","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2024031710030025800_ref16","first-page":"2022","article-title":"Multi-modal single-cell and whole-genome sequencing of minute, frozen specimens to propel clinical applications","author":"Wang","year":"2022"},{"key":"2024031710030025800_ref17","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa287","article-title":"Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data","volume":"22","author":"Zuo","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1","key":"2024031710030025800_ref18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-021-02556-z","article-title":"Cobolt: integrative analysis of multimodal single-cell sequencing data","volume":"22","author":"Gong","year":"2021","journal-title":"Genome Biol"},{"issue":"21","key":"2024031710030025800_ref19","doi-asserted-by":"crossref","first-page":"e121","DOI":"10.1093\/nar\/gkac781","article-title":"Integrated analysis of multimodal single-cell data with structural similarity","volume":"50","author":"Cao","year":"2022","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"2024031710030025800_ref20","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1089\/cmb.2021.0596","article-title":"Translator: a transfer learning approach to facilitate single-cell at AC-seq data analysis from reference dataset","volume":"29","author":"Siwei","year":"2022","journal-title":"J Comput Biol"},{"issue":"22","key":"2024031710030025800_ref21","doi-asserted-by":"crossref","first-page":"4091","DOI":"10.1093\/bioinformatics\/btab403","article-title":"Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data","volume":"37","author":"Zuo","year":"2021","journal-title":"Bioinformatics"},{"issue":"4","key":"2024031710030025800_ref22","doi-asserted-by":"crossref","first-page":"btad133","DOI":"10.1093\/bioinformatics\/btad133","article-title":"scMCs: a framework for single-cell multi-omics data integration and multiple clusterings","volume":"39","author":"Ren","year":"2023","journal-title":"Bioinformatics"},{"issue":"1","key":"2024031710030025800_ref23","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1186\/s13059-022-02706-x","article-title":"scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously","volume":"23","author":"Zhang","year":"2022","journal-title":"Genome Biol"},{"issue":"1","key":"2024031710030025800_ref24","doi-asserted-by":"crossref","first-page":"7705","DOI":"10.1038\/s41467-022-35031-9","article-title":"Clustering of single-cell multi-omics data with a multimodal deep learning method","volume":"13","author":"Lin","year":"2022","journal-title":"Nat Commun"},{"issue":"19","key":"2024031710030025800_ref25","doi-asserted-by":"crossref","first-page":"e7045","DOI":"10.1002\/cpe.7045","article-title":"A new stein estimator for the Zero-Inflated Negative Binomial regression model","volume":"34","author":"Akram","year":"2022","journal-title":"Concurr Comput: Pract Exp"},{"issue":"1","key":"2024031710030025800_ref26","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1007\/s13171-022-00286-3","article-title":"Jeffreys prior for negative binomial and zero inflated negative binomial distributions","volume":"85","author":"Maity","year":"2023","journal-title":"Sankhya A"},{"issue":"6","key":"2024031710030025800_ref27","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1177\/03611981221148703","article-title":"Modeling of parking violations using Zero-Inflated Negative Binomial regression: a case study for berlin","volume":"2677","author":"Hagen","year":"2023","journal-title":"Transp Res Rec"},{"key":"2024031710030025800_ref28","article-title":"Transformer for graphs: an overview from architecture perspective","author":"Min","year":"2022"},{"key":"2024031710030025800_ref29","first-page":"668","article-title":"Flowformer: a transformer architecture for optical flow","volume-title":"European Conference on Computer Vision","author":"Huang","year":"2022"},{"key":"2024031710030025800_ref30","first-page":"10894","article-title":"Training-free transformer architecture search","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhou","year":"2022"},{"issue":"2","key":"2024031710030025800_ref31","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1080\/03610926.2021.1916531","article-title":"A mixture autoregressive model based on student\u2019s t-distribution","volume":"52","author":"Meitz","year":"2023","journal-title":"Commun Statist-Theory Methods"},{"key":"2024031710030025800_ref32","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TSP.2022.3151199","article-title":"An outlier-robust Kalman filter with adaptive selection of elliptically contoured distributions","volume":"70","author":"Xue","year":"2022","journal-title":"IEEE Trans Signal Process"},{"key":"2024031710030025800_ref33","doi-asserted-by":"crossref","first-page":"120011","DOI":"10.1016\/j.envpol.2022.120011","article-title":"Microplastic distribution and composition on two Gal\u00e1pagos Island Beaches, Ecuador: verifying the use of citizen science derived data in long-term monitoring","volume":"311","author":"Jones","year":"2022","journal-title":"Environ Pollut"},{"key":"2024031710030025800_ref34","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac630","article-title":"Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding","volume":"24","author":"Yuan","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024031710030025800_ref35","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad159","article-title":"Benchmarking of analytical combinations for Covid-19 outcome prediction using single-cell RNA sequencing data","volume":"24","author":"Cao","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024031710030025800_ref36","first-page":"elad007","article-title":"ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning","author":"Bai","year":"2023","journal-title":"Brief Funct Genomics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/2\/bbae102\/56995105\/bbae102.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/2\/bbae102\/56995105\/bbae102.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T10:03:31Z","timestamp":1710669811000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae102\/7630472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae102","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,3,1]]},"published":{"date-parts":[[2024,1,22]]},"article-number":"bbae102"}}