{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T04:04:30Z","timestamp":1786421070336,"version":"3.56.0"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T00:00:00Z","timestamp":1589587200000},"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\/501100003554","name":"Lundbeck Foundation","doi-asserted-by":"publisher","award":["R190 2014-3904"],"award-info":[{"award-number":["R190 2014-3904"]}],"id":[{"id":"10.13039\/501100003554","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"publisher","award":["NNF18CC0034900"],"award-info":[{"award-number":["NNF18CC0034900"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011747","name":"Novo Nordisk Foundation Center for Basic Metabolic Research","doi-asserted-by":"publisher","award":["NNF16OC0021496"],"award-info":[{"award-number":["NNF16OC0021496"]}],"id":[{"id":"10.13039\/501100011747","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Danish Ministry of Higher Education and Science [Elite Research Travel Grant 2018]"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Models for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter choices. With deep generative models one can work directly with count data, make likelihood-based model comparison, learn a latent representation of the cells and capture more of the variability in different cell populations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a novel method based on variational auto-encoders (VAEs) for analysis of single-cell RNA sequencing (scRNA-seq) data. It avoids data preprocessing by using raw count data as input and can robustly estimate the expected gene expression levels and a latent representation for each cell. We tested several count likelihood functions and a variant of the VAE that has a priori clustering in the latent space. We show for several scRNA-seq datasets that our method outperforms recently proposed scRNA-seq methods in clustering cells and that the resulting clusters reflect cell types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Our method, called scVAE, is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https:\/\/github.com\/scvae\/scvae.<\/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\/btaa293","type":"journal-article","created":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T09:21:44Z","timestamp":1589275304000},"page":"4415-4422","source":"Crossref","is-referenced-by-count":247,"title":["scVAE: variational auto-encoders for single-cell gene expression data"],"prefix":"10.1093","volume":"36","author":[{"given":"Christopher Heje","family":"Gr\u00f8nbech","sequence":"first","affiliation":[{"name":"Department of Biology, Bioinformatics Centre, University of Copenhagen"},{"name":"Centre for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital , K\u00f8benhavn \u00d8 2100, Denmark"},{"name":"Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark , Kongens Lyngby 2800, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximillian Fornitz","family":"Vording","sequence":"additional","affiliation":[{"name":"Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark , Kongens Lyngby 2800, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal N","family":"Timshel","sequence":"additional","affiliation":[{"name":"Faculty of Health and Medical Sciences, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen , K\u00f8benhavn N 2200, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Casper Kaae","family":"S\u00f8nderby","sequence":"additional","affiliation":[{"name":"Department of Biology, Bioinformatics Centre, University of Copenhagen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tune H","family":"Pers","sequence":"additional","affiliation":[{"name":"Faculty of Health and Medical Sciences, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen , K\u00f8benhavn N 2200, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ole","family":"Winther","sequence":"additional","affiliation":[{"name":"Department of Biology, Bioinformatics Centre, University of Copenhagen"},{"name":"Centre for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital , K\u00f8benhavn \u00d8 2100, Denmark"},{"name":"Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark , Kongens Lyngby 2800, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,5,16]]},"reference":[{"key":"2023062213531253500_btaa293-B1","author":"Abadi","year":"2015"},{"key":"2023062213531253500_btaa293-B2","author":"Bowman","year":"2016"},{"key":"2023062213531253500_btaa293-B3","first-page":"557","author":"Brouwer","year":"2017"},{"key":"2023062213531253500_btaa293-B4","doi-asserted-by":"crossref","first-page":"S9","DOI":"10.1186\/s12859-015-0852-1","article-title":"Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model","volume":"17","author":"Chen","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2023062213531253500_btaa293-B5","author":"Cui","year":"2017"},{"key":"2023062213531253500_btaa293-B6","author":"Dilokthanakul","year":"2016"},{"key":"2023062213531253500_btaa293-B7","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1038\/s41467-018-04368-5","article-title":"Interpretable dimensionality reduction of single cell transcriptome data with deep generative models","volume":"9","author":"Ding","year":"2018","journal-title":"Nat. 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