{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:05Z","timestamp":1772138045595,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004853","name":"Chinese University of Hong Kong","doi-asserted-by":"publisher","award":["4053360"],"award-info":[{"award-number":["4053360"]}],"id":[{"id":"10.13039\/501100004853","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004853","name":"Chinese University of Hong Kong","doi-asserted-by":"publisher","award":["4053423"],"award-info":[{"award-number":["4053423"]}],"id":[{"id":"10.13039\/501100004853","id-type":"DOI","asserted-by":"publisher"}]},{"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":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The advancement in technologies and the growth of available single-cell datasets motivate integrative analysis of multiple single-cell genomic datasets. Integrative analysis of multimodal single-cell datasets combines complementary information offered by single-omic datasets and can offer deeper insights on complex biological process. Clustering methods that identify the unknown cell types are among the first few steps in the analysis of single-cell datasets, and they are important for downstream analysis built upon the identified cell types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose scAMACE for the integrative analysis and clustering of single-cell data on chromatin accessibility, gene expression and methylation. We demonstrate that cell types are better identified and characterized through analyzing the three data types jointly. We develop an efficient Expectation\u2013Maximization algorithm to perform statistical inference, and evaluate our methods on both simulation study and real data applications. We also provide the GPU implementation of scAMACE, making it scalable to large datasets.<\/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\/cuhklinlab\/scAMACE_py (python implementation) and https:\/\/github.com\/cuhklinlab\/scAMACE (R implementation).<\/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\/btab426","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T08:49:33Z","timestamp":1622710173000},"page":"3874-3880","source":"Crossref","is-referenced-by-count":11,"title":["scAMACE: model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7924-4486","authenticated-orcid":false,"given":"Jiaxuan","family":"Wangwu","sequence":"first","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zexuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Hong Kong SAR, 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":[[2021,6,4]]},"reference":[{"key":"2023051608165303100_btab426-B1","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":"Bravo Gonz\u00e1lez-Blas","year":"2019","journal-title":"Nat. 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