{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:12:33Z","timestamp":1775117553859,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2019YFA0709501"],"award-info":[{"award-number":["2019YFA0709501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61733018"],"award-info":[{"award-number":["61733018"]}],"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":["12071466"],"award-info":[{"award-number":["12071466"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major","award":["2021SHZDZX010"],"award-info":[{"award-number":["2021SHZDZX010"]}]},{"name":"Fundamental Research Funds for the Central Universities and LSC of CAS"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Single-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Existing manifold alignment methods demonstrated the state-of-the-art performance on single-cell multi-omics data integration, but they are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we present Pamona, a partial Gromov-Wasserstein distance-based manifold alignment framework that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures across modalities. We formulate this task as a partial manifold alignment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. We evaluated Pamona on a comprehensive set of publicly available benchmark datasets. We demonstrated that Pamona can accurately identify shared and dataset-specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in a common space, outperforming the comparable existing methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availabilityand implementation<\/jats:title>\n                    <jats:p>Pamona software is available at https:\/\/github.com\/caokai1073\/Pamona.<\/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\/btab594","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T07:09:47Z","timestamp":1628838587000},"page":"211-219","source":"Crossref","is-referenced-by-count":91,"title":["Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona"],"prefix":"10.1093","volume":"38","author":[{"given":"Kai","family":"Cao","sequence":"first","affiliation":[{"name":"LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing 100190, China"},{"name":"School of Mathematical Sciences, University of Chinese Academy of Sciences , Beijing 100049, China"}]},{"given":"Yiguang","family":"Hong","sequence":"additional","affiliation":[{"name":"LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing 100190, China"},{"name":"Department of Control Science and Engineering, Tongji University , Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3511-0512","authenticated-orcid":false,"given":"Lin","family":"Wan","sequence":"additional","affiliation":[{"name":"LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing 100190, China"},{"name":"School of Mathematical Sciences, University of Chinese Academy of Sciences , Beijing 100049, China"}]}],"member":"286","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"2023020108395620800_btab594-B1","doi-asserted-by":"crossref","first-page":"e8124","DOI":"10.15252\/msb.20178124","article-title":"Multi-omics factor analysis\u2014a framework for unsupervised integration of multi-omics data sets","volume":"14","author":"Argelaguet","year":"2018","journal-title":"Mol. 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