{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:27:15Z","timestamp":1783488435845,"version":"3.55.0"},"reference-count":10,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"J.D.W.","award":["R01HG010883"],"award-info":[{"award-number":["R01HG010883"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>LIGER (Linked Inference of Genomic Experimental Relationships) is a widely used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly optimized scientific computing libraries for increased efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2\u20135\u00d7 speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>PyLiger is available on Github at https:\/\/github.com\/welch-lab\/pyliger and on the Python Package Index.<\/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\/btac190","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T15:14:45Z","timestamp":1648566885000},"page":"2946-2948","source":"Crossref","is-referenced-by-count":17,"title":["PyLiger: scalable single-cell multi-omic data integration in Python"],"prefix":"10.1093","volume":"38","author":[{"given":"Lu","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5869-2391","authenticated-orcid":false,"given":"Joshua D","family":"Welch","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI, USA"},{"name":"Department of Computer Science and Engineering, University of Michigan , Ann Arbor, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"2023020109103006100_btac190-B1","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1038\/s41587-020-0591-3","article-title":"Generalizing RNA velocity to transient cell states through dynamical modeling","volume":"38","author":"Bergen","year":"2020","journal-title":"Nat. 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