{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T05:38:16Z","timestamp":1775281096188,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HPC Platform of ShanghaiTech University"},{"DOI":"10.13039\/501100004853","name":"Chinese University of Hong Kong","doi-asserted-by":"publisher","award":["4930181"],"award-info":[{"award-number":["4930181"]}],"id":[{"id":"10.13039\/501100004853","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chinese University of Hong Kong\u2019s Project Impact Enhancement Fund"},{"name":"Science Faculty\u2019s Collaborative Research Impact Matching Scheme"},{"name":"Hong Kong Research Grant Council","award":["ECS 24301419"],"award-info":[{"award-number":["ECS 24301419"]}]},{"name":"Hong Kong Research Grant Council","award":["GRF 14301120"],"award-info":[{"award-number":["GRF 14301120"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available protocols and multi-omics datasets [including parallel single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data profiled from the same cell] are growing increasingly. However, such data are highly sparse and tend to have high level of noise, making data analysis challenging. The methods that integrate the multi-omics data can potentially improve the capacity of revealing the cellular heterogeneity.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose an adaptively weighted multi-view learning (scAWMV) method for the integrative analysis of parallel scRNA-seq and scATAC-seq data profiled from the same cell. scAWMV considers both the difference in importance across different modalities in multi-omics data and the biological connection of the features in the scRNA-seq and scATAC-seq data. It generates biologically meaningful low-dimensional representations for the transcriptomic and epigenomic profiles via unsupervised learning. Application to four real datasets demonstrates that our framework scAWMV is an efficient method to dissect cellular heterogeneity for single-cell multi-omics data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The software and datasets are available at https:\/\/github.com\/pengchengzeng\/scAWMV.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac739","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T15:42:39Z","timestamp":1668613359000},"source":"Crossref","is-referenced-by-count":7,"title":["scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0724-5313","authenticated-orcid":false,"given":"Pengcheng","family":"Zeng","sequence":"first","affiliation":[{"name":"Institute of Mathematical Sciences, ShanghaiTech University , Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9103-5513","authenticated-orcid":false,"given":"Yuanyuan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Anyang Normal University , Henan 455000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"2023010107522495400_btac739-B1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1038\/nmeth.3728","article-title":"Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity","volume":"13","author":"Angermueller","year":"2016","journal-title":"Nat. Methods"},{"key":"2023010107522495400_btac739-B2","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1177\/17534259211001512","article-title":"Human NK cells: from development to effector functions","volume":"27","author":"Arachchige","year":"2021","journal-title":"Innate Immun"},{"key":"2023010107522495400_btac739-B3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s13059-020-02015-1","article-title":"Mofa+: a statistical framework for comprehensive integration of multi-modal single-cell data","volume":"21","author":"Argelaguet","year":"2020","journal-title":"Genome Biol"},{"key":"2023010107522495400_btac739-B4","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech"},{"key":"2023010107522495400_btac739-B5","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1073\/pnas.0308531101","article-title":"Metagenes and molecular pattern discovery using matrix factorization","volume":"101","author":"Brunet","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023010107522495400_btac739-B6","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat. Biotechnol"},{"key":"2023010107522495400_btac739-B7","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1126\/science.aau0730","article-title":"Joint profiling of chromatin accessibility and gene expression in thousands of single cells","volume":"361","author":"Cao","year":"2018","journal-title":"Science"},{"key":"2023010107522495400_btac739-B8","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/s41587-019-0290-0","article-title":"High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell","volume":"37","author":"Chen","year":"2019","journal-title":"Nat. Biotechnol"},{"key":"2023010107522495400_btac739-B10","volume-title":"Introduction to Information Retrieval","author":"Christopher","year":"2008"},{"key":"2023010107522495400_btac739-B11","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1038\/s41467-018-03149-4","article-title":"scNMT-seq enables joint profiling of chromatin accessibility, DNA methylation and transcription in single cells","volume":"9","author":"Clark","year":"2018","journal-title":"Nat. Commun"},{"key":"2023010107522495400_btac739-B12","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.coisb.2018.01.003","article-title":"Statistical single cell multi-omics integration","volume":"7","author":"Colom\u00e9-Tatch\u00e9","year":"2018","journal-title":"Curr. Opin. Syst. Biol"},{"key":"2023010107522495400_btac739-B13","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.cell.2018.06.052","article-title":"A single-cell atlas of in vivo mammalian chromatin accessibility","volume":"174","author":"Cusanovich","year":"2018","journal-title":"Cell"},{"key":"2023010107522495400_btac739-B14","doi-asserted-by":"crossref","first-page":"7723","DOI":"10.1073\/pnas.1805681115","article-title":"Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorization","volume":"115","author":"Duren","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023010107522495400_btac739-B15","first-page":"769","article-title":"Concentration and dependency ratios","volume":"87","author":"Gini","year":"1997","journal-title":"Riv. Polit. Econ"},{"key":"2023010107522495400_btac739-B16","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1038\/s41592-019-0367-1","article-title":"Cistopic: cis-regulatory topic modeling on single-cell ATAC-seq data","volume":"16","author":"Gonzalez-Blas","year":"2019","journal-title":"Nat. Methods"},{"key":"2023010107522495400_btac739-B17","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2023010107522495400_btac739-B18","doi-asserted-by":"crossref","first-page":"D947","DOI":"10.1093\/nar\/gkaa609","article-title":"HRT atlas v1.0 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets","volume":"49","author":"Hounkpe","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023010107522495400_btac739-B19","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s13059-020-1932-8","article-title":"scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles","volume":"21","author":"Jin","year":"2020","journal-title":"Genome Biol"},{"key":"2023010107522495400_btac739-B20","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"},{"key":"2023010107522495400_btac739-B21","first-page":"2","article-title":"Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression","volume":"35","author":"Lin","year":"2019","journal-title":"Stat. Sci"},{"key":"2023010107522495400_btac739-B22","first-page":"252","author":"Liu","year":"2013"},{"key":"2023010107522495400_btac739-B23","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1038\/s41467-018-08205-7","article-title":"Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity","volume":"10","author":"Liu","year":"2019","journal-title":"Nat. Commnun"},{"key":"2023010107522495400_btac739-B9","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1016\/j.cell.2020.09.056","article-title":"Chromatin potential identified by shared single-cell profiling of RNA and chromatin","volume":"183","author":"Ma","year":"2020","journal-title":"Cell"},{"key":"2023010107522495400_btac739-B24","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac105","article-title":"JSNMF enables effective and accurate integrative analysis of single-cell multiomics data","volume":"23","author":"Ma","year":"2022","journal-title":"Brief Bioinformatics"},{"key":"2023010107522495400_btac739-B25","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.tig.2016.12.003","article-title":"Single-cell multiomics: multiple measurements from single cells","volume":"33","author":"Macaulay","year":"2017","journal-title":"Trends Genet"},{"key":"2023010107522495400_btac739-B26","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/s41467-018-05887-x","article-title":"High-throughout chromatin accessibility profiling at single-cell resolution","volume":"9","author":"Mezger","year":"2018","journal-title":"Nat. Commun"},{"key":"2023010107522495400_btac739-B27","volume-title":"Janeway\u2019s Immunobiology","author":"Murphy","year":"2012","edition":"8th edn"},{"key":"2023010107522495400_btac739-B28","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1038\/nbt.3383","article-title":"Single-cell chip-seq reveals cell subpopulations defined by chromatin state","volume":"33","author":"Rotem","year":"2015","journal-title":"Nat. Biotechnol"},{"key":"2023010107522495400_btac739-B29","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/550451a","article-title":"The human cell atlas: from vision to reality","volume":"550","author":"Rozenblatt-Rosen","year":"2017","journal-title":"Nat. News"},{"key":"2023010107522495400_btac739-B30","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","article-title":"Comprehensive integration of single-cell data","volume":"177","author":"Stuart","year":"2019","journal-title":"Cell"},{"key":"2023010107522495400_btac739-B31","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1101\/gr.254557.119","article-title":"SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection","volume":"30","author":"Wan","year":"2020","journal-title":"Genome Res"},{"key":"2023010107522495400_btac739-B32","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/nmeth.4207","article-title":"Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning","volume":"14","author":"Wang","year":"2017","journal-title":"Nat. Methods"},{"key":"2023010107522495400_btac739-B33","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TKDE.2012.51","article-title":"Nonnegative matrix factorization: a comprehensive review","volume":"25","author":"Wang","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"2023010107522495400_btac739-B34","doi-asserted-by":"crossref","first-page":"3874","DOI":"10.1093\/bioinformatics\/btab426","article-title":"scAMACE: model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation","volume":"37","author":"Wangwu","year":"2021","journal-title":"Bioinformatics"},{"key":"2023010107522495400_btac739-B35","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1016\/j.cell.2019.05.006","article-title":"Single-cell multi-omic integration compares and contrasts features of brain cell identity","volume":"177","author":"Welch","year":"2019","journal-title":"Cell"},{"key":"2023010107522495400_btac739-B36","doi-asserted-by":"crossref","first-page":"4576","DOI":"10.1038\/s41467-019-12630-7","article-title":"SCALE method for single-cell ATAC-seq analysis via latent feature extraction","volume":"10","author":"Xiong","year":"2019","journal-title":"Nat. Commun"},{"key":"2023010107522495400_btac739-B37","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1093\/bioinformatics\/bty793","article-title":"Safe-clustering: single-cell aggregated (from ensemble) clustering for single-cell RNA-seq data","volume":"35","author":"Yang","year":"2018","journal-title":"Bioinformatics"},{"key":"2023010107522495400_btac739-B38","doi-asserted-by":"crossref","first-page":"2410","DOI":"10.1038\/s41467-018-04629-3","article-title":"Unsupervised clustering and epigenetic classification of single cells","volume":"9","author":"Zamanighomi","year":"2018","journal-title":"Nat. Commun"},{"key":"2023010107522495400_btac739-B39","doi-asserted-by":"crossref","first-page":"e1009064","DOI":"10.1371\/journal.pcbi.1009064","article-title":"Couplecoc+: an information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data","volume":"17","author":"Zeng","year":"2021","journal-title":"PLoS Comput. Biol"},{"key":"2023010107522495400_btac739-B40","article-title":"Coupled co-clustering-based unsupervised transfer learning for the integrative analysis of single-cell genomics data","volume":"22","author":"Zeng","year":"2020","journal-title":"Brief Bioinformatics"},{"key":"2023010107522495400_btac739-B41","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1093\/nar\/gky900","article-title":"Cellmarkers: a manually curated resource of cell markers in human and mouse","volume":"47","author":"Zhang","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023010107522495400_btac739-B42","first-page":"1","article-title":"Metascape provides a biologist-oriented resource for the analysis of systems-level datasets","volume":"10","author":"Zhou","year":"2019","journal-title":"Nat. Commun"},{"key":"2023010107522495400_btac739-B43","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1038\/s41594-019-0323-x","article-title":"An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome","volume":"26","author":"Zhu","year":"2019","journal-title":"Nat. Struct. Mol. Biol"},{"key":"2023010107522495400_btac739-B44","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s41592-021-01060-3","article-title":"Joint profiling of histone modifications and transcriptome in single cells from mouse brain","volume":"18","author":"Zhu","year":"2021","journal-title":"Nat. Methods"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac739\/47194083\/btac739.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/1\/btac739\/48448772\/btac739.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/1\/btac739\/48448772\/btac739.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T10:10:27Z","timestamp":1672567827000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btac739\/6831091"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,11,16]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,11,16]]},"published-print":{"date-parts":[[2023,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac739","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1,1]]},"published":{"date-parts":[[2022,11,16]]},"article-number":"btac739"}}