{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T13:18:03Z","timestamp":1776777483304,"version":"3.51.2"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T00:00:00Z","timestamp":1606953600000},"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\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"crossref","award":["RGPIN-2019-0621"],"award-info":[{"award-number":["RGPIN-2019-0621"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fonds de recherche Nature et technologies"},{"name":"New Career","award":["NC-268592"],"award-info":[{"award-number":["NC-268592"]}]},{"name":"Canada First Research Excellence Fund Healthy Brains for Healthy Life"},{"name":"initiative New Investigator award","award":["G249591"],"award-info":[{"award-number":["G249591"]}]},{"name":"IR National Science Foundation","award":["96006077"],"award-info":[{"award-number":["96006077"]}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institute of Health Research","doi-asserted-by":"crossref","award":["FDN148374"],"award-info":[{"award-number":["FDN148374"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Single-cell RNA-sequencing (scRNA-seq) offers the opportunity to dissect heterogeneous cellular compositions and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition designs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we present a single-cell Generative Adversarial Network (scGAN) to simultaneously acquire patterns from raw data while minimizing the confounding effect driven by technical artifacts or other factors inherent to the data. Specifically, scGAN models the data likelihood of the raw scRNA-seq counts by projecting each cell onto a latent embedding. Meanwhile, scGAN attempts to minimize the correlation between the latent embeddings and the batch labels across all cells. We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availabilityand implementation<\/jats:title>\n                    <jats:p>The scGAN code and the information for the public scRNA-seq datasets are available at https:\/\/github.com\/li-lab-mcgill\/singlecell-deepfeature.<\/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\/btaa976","type":"journal-article","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T07:11:49Z","timestamp":1605078709000},"page":"1345-1351","source":"Crossref","is-referenced-by-count":33,"title":["Deep feature extraction of single-cell transcriptomes by generative adversarial network"],"prefix":"10.1093","volume":"37","author":[{"given":"Mojtaba","family":"Bahrami","sequence":"first","affiliation":[{"name":"School of Computer Science, McGill Centre for Bioinformatics, McGill University, Montreal, QC H3A 0E9, Canada"},{"name":"Department of Computer Engineering, Sharif University of Technology, Tehran 11365-11155, Iran"}]},{"given":"Malosree","family":"Maitra","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, McGill Group for Suicide Studies, Douglas Mental Health University, Montreal, QC H4H 1R3, Canada"},{"name":"Integrated Program in Neuroscience, McGill University, Montreal QC, H3A 2B4, Canada"}]},{"given":"Corina","family":"Nagy","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, McGill Group for Suicide Studies, Douglas Mental Health University, Montreal, QC H4H 1R3, Canada"}]},{"given":"Gustavo","family":"Turecki","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, McGill Group for Suicide Studies, Douglas Mental Health University, Montreal, QC H4H 1R3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9835-4493","authenticated-orcid":false,"given":"Hamid R","family":"Rabiee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Sharif University of Technology, Tehran 11365-11155, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3844-4865","authenticated-orcid":false,"given":"Yue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, McGill Centre for Bioinformatics, McGill University, Montreal, QC H3A 0E9, Canada"}]}],"member":"286","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"2021061619235971200_btaa976-B1","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell Syst"},{"key":"2021061619235971200_btaa976-B2","first-page":"1","volume":"67","journal-title":"J. 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