{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:31:05Z","timestamp":1790634665664,"version":"4.1.0"},"reference-count":9,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"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\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01CA177669"],"award-info":[{"award-number":["R01CA177669"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["U01CA196390"],"award-info":[{"award-number":["U01CA196390"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["U01CA212007"],"award-info":[{"award-number":["U01CA212007"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IOS-1656592"],"award-info":[{"award-number":["IOS-1656592"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chan-Zuckerberg Initiative DAF","award":["2018-183445"],"award-info":[{"award-number":["2018-183445"]}]},{"name":"Chan-Zuckerberg Initiative DAF","award":["2018-183444"],"award-info":[{"award-number":["2018-183444"]}]},{"DOI":"10.13039\/100000923","name":"Silicon Valley Community Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Johns Hopkins University Provost Fellowship"},{"name":"Johns Hopkins University School of Medicine Synergy Award"},{"name":"Allegheny Foundation"},{"DOI":"10.13039\/100005979","name":"Lustgarten Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005979","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Emerson Foundation","award":["640183"],"award-info":[{"award-number":["640183"]}]},{"name":"Kavli Institute"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Dimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically importent in analysis of large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed an R\/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>projectR is available on Bioconductor and at https:\/\/github.com\/genesofeve\/projectR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Contact<\/jats:title>\n                    <jats:p>gsteinobrien@jhmi.edu or ejfertig@jhmi.edu<\/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\/btaa183","type":"journal-article","created":{"date-parts":[[2020,3,11]],"date-time":"2020-03-11T01:12:46Z","timestamp":1583889166000},"page":"3592-3593","source":"Crossref","is-referenced-by-count":85,"title":["projectR: an R\/Bioconductor package for transfer learning via PCA, NMF, correlation and clustering"],"prefix":"10.1093","volume":"36","author":[{"given":"Gaurav","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Colantuoni","sequence":"additional","affiliation":[{"name":"Department of Neuroscience"},{"name":"Department of Neurology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Loyal A","family":"Goff","sequence":"additional","affiliation":[{"name":"Department of Neuroscience"},{"name":"Kavli Neurodiscovery Institute"},{"name":"Department of Genetic Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3204-342X","authenticated-orcid":false,"given":"Elana J","family":"Fertig","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering"},{"name":"Department of Oncology"},{"name":"Department of Applied Mathematics and Statistics , Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genevieve","family":"Stein-O\u2019Brien","sequence":"additional","affiliation":[{"name":"Department of Neuroscience"},{"name":"Kavli Neurodiscovery Institute"},{"name":"Department of Genetic Medicine"},{"name":"Department of Oncology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"2023062312021561200_btaa183-B9","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1016\/j.cell.2017.10.023","article-title":"Efficient generation of transcriptomic profiles by random composite measurements","volume":"171","author":"Cleary","year":"2017","journal-title":"Cell"},{"key":"2023062312021561200_btaa183-B1","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.1093\/bioinformatics\/btq503","article-title":"CoGAPS: an R\/C\u00b1+ package to identify patterns and biological process activity in transcriptomic data","volume":"26","author":"Fertig","year":"2010","journal-title":"Bioinformatics"},{"key":"2023062312021561200_btaa183-B2","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.cell.2008.01.053","article-title":"A quantitative spatiotemporal atlas of gene expression in the Drosophila blastoderm","volume":"133","author":"Fowlkes","year":"2008","journal-title":"Cell"},{"key":"2023062312021561200_btaa183-B3","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1126\/science.aan3235","article-title":"The Drosophila embryo at single-cell transcriptome resolution","volume":"358","author":"Karaiskos","year":"2017","journal-title":"Science"},{"key":"2023062312021561200_btaa183-B4","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1093\/bib\/bbv108","article-title":"Dimension reduction techniques for the integrative analysis of multi-omics data","volume":"17","author":"Meng","year":"2016","journal-title":"Brief. 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