{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:48:52Z","timestamp":1772776132027,"version":"3.50.1"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"la Caixa\u2019 Foundation","award":["100010434"],"award-info":[{"award-number":["100010434"]}]},{"name":"la Caixa\u2019 Foundation","award":["LCF\/BQ\/EU19\/11710059"],"award-info":[{"award-number":["LCF\/BQ\/EU19\/11710059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>High-throughput gene expression can be used to address a wide range of fundamental biological problems, but datasets of an appropriate size are often unavailable. Moreover, existing transcriptomics simulators have been criticized because they fail to emulate key properties of gene expression data. In this article, we develop a method based on a conditional generative adversarial network to generate realistic transcriptomics data for Escherichia coli and humans. We assess the performance of our approach across several tissues and cancer-types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We show that our model preserves several gene expression properties significantly better than widely used simulators, such as SynTReN or GeneNetWeaver. The synthetic data preserve tissue- and cancer-specific properties of transcriptomics data. Moreover, it exhibits real gene clusters and ontologies both at local and global scales, suggesting that the model learns to approximate the gene expression manifold in a biologically meaningful way.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Code is available at: https:\/\/github.com\/rvinas\/adversarial-gene-expression.<\/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\/btab035","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T15:33:31Z","timestamp":1610724811000},"page":"730-737","source":"Crossref","is-referenced-by-count":37,"title":["Adversarial generation of gene expression data"],"prefix":"10.1093","volume":"38","author":[{"given":"Ramon","family":"Vi\u00f1as","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge , Cambridge, UK"},{"name":"Department of Computer Science, University College London , London, UK"}]},{"given":"Helena","family":"Andr\u00e9s-Terr\u00e9","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge , Cambridge, UK"}]},{"given":"Pietro","family":"Li\u00f2","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge , Cambridge, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1163-6368","authenticated-orcid":false,"given":"Kevin","family":"Bryson","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University College London , London, UK"}]}],"member":"286","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"2023020108471601400_btab035-B1","first-page":"1318","article-title":"The GTEx consortium atlas of genetic regulatory effects across human tissues","author":"Aguet","year":"2019","journal-title":"Science,"},{"key":"2023020108471601400_btab035-B2","article-title":"Face aging with conditional generative adversarial networks","author":"Antipov","year":"2017","journal-title":"IEEE International Conference on Image Processing (ICIP), Beijing, China, pp. 2089\u20132093"},{"key":"2023020108471601400_btab035-B3","first-page":"arXiv:1701.07875","article-title":"Wasserstein GAN","author":"Arjovsky","year":"2017","journal-title":"arXiv e-Prints"},{"key":"2023020108471601400_btab035-B4","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.cell.2018.02.060","article-title":"Comprehensive characterization of cancer driver genes and mutations","volume":"173","author":"Bailey","year":"2018","journal-title":"Cell"},{"key":"2023020108471601400_btab035-B5","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1038\/s41559-019-0996-x","article-title":"Inferred divergent gene regulation in archaic hominins reveals potential phenotypic differences","volume":"3","author":"Colbran","year":"2019","journal-title":"Nat. Ecol. Evol"},{"key":"2023020108471601400_btab035-B6","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1038\/nrg2537","article-title":"Mapping complex disease traits with global gene expression","volume":"10","author":"Cookson","year":"2009","journal-title":"Nat. Rev. Genet"},{"key":"2023020108471601400_btab035-B7","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1038\/nature06758","article-title":"Genetics of gene expression and its effect on disease","volume":"452","author":"Emilsson","year":"2008","journal-title":"Nature"},{"key":"2023020108471601400_btab035-B8","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1038\/nature02626","article-title":"Moving towards individualized medicine with pharmacogenomics","volume":"429","author":"Evans","year":"2004","journal-title":"Nature"},{"key":"2023020108471601400_btab035-B9","doi-asserted-by":"crossref","first-page":"D866","DOI":"10.1093\/nar\/gkm815","article-title":"Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata","volume":"36","author":"Faith","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2023020108471601400_btab035-B10","doi-asserted-by":"crossref","first-page":"D133","DOI":"10.1093\/nar\/gkv1156","article-title":"RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond","volume":"44","author":"Gama-Castro","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023020108471601400_btab035-B11","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1038\/s41588-018-0154-4","article-title":"Using an atlas of gene regulation across 44 human tissues to inform complex disease-and trait-associated variation","volume":"50","author":"Gamazon","year":"2018","journal-title":"Nat. Genet"},{"key":"2023020108471601400_btab035-B12","first-page":"2672","author":"Goodfellow","year":"2014"},{"key":"2023020108471601400_btab035-B13","author":"Grote","year":"2020"},{"key":"2023020108471601400_btab035-B14","article-title":"Improved training of Wasserstein GANs","author":"Gulrajani","year":"2017","journal-title":"CoRR"},{"key":"2023020108471601400_btab035-B15","doi-asserted-by":"crossref","first-page":"e15","DOI":"10.1093\/nar\/gng015","article-title":"Summaries of Affymetrix GeneChip probe level data","volume":"31","author":"Irizarry","year":"2003","journal-title":"Nucleic Acids Res"},{"key":"2023020108471601400_btab035-B16","doi-asserted-by":"crossref","first-page":"e12776","DOI":"10.1371\/journal.pone.0012776","article-title":"Inferring regulatory networks from expression data using tree-based methods","volume":"5","author":"Irrthum","year":"2010","journal-title":"PLoS One"},{"key":"2023020108471601400_btab035-B17","first-page":"8107","author":"Karras","year":"2020"},{"key":"2023020108471601400_btab035-B18","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.1093\/bioinformatics\/btt438","article-title":"A Turing test for artificial expression data","volume":"29","author":"Maier","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020108471601400_btab035-B19","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2105-7-S1-S7","article-title":"ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context","volume":"7","author":"Margolin","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023020108471601400_btab035-B20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-14018-z","article-title":"Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks","volume":"11","author":"Marouf","year":"2020","journal-title":"Nat. Commun"},{"key":"2023020108471601400_btab035-B21","author":"McInnes","year":"2020"},{"key":"2023020108471601400_btab035-B22","first-page":"3111","author":"Mikolov","year":"2013"},{"key":"2023020108471601400_btab035-B23","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1038\/nmeth.1226","article-title":"Mapping and quantifying mammalian transcriptomes by RNA-seq","volume":"5","author":"Mortazavi","year":"2008","journal-title":"Nat. Methods"},{"key":"2023020108471601400_btab035-B24","article-title":"Invertible conditional GANs for image editing","author":"Perarnau","year":"2016","journal-title":"NIPS Workshop on Adversarial Training."},{"key":"2023020108471601400_btab035-B25","doi-asserted-by":"crossref","first-page":"D394","DOI":"10.1093\/nar\/gkj156","article-title":"RegulonDB (version 5.0): Escherichia coli k-12 transcriptional regulatory network, operon organization, and growth conditions","volume":"34","author":"Salgado","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2023020108471601400_btab035-B26","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1093\/bioinformatics\/btr373","article-title":"GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods","volume":"27","author":"Schaffter","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020108471601400_btab035-B27","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1126\/science.270.5235.467","article-title":"Quantitative monitoring of gene expression patterns with a complementary DNA microarray","volume":"270","author":"Schena","year":"1995","journal-title":"Science"},{"key":"2023020108471601400_btab035-B28","doi-asserted-by":"crossref","first-page":"96ra77","DOI":"10.1126\/scitranslmed.3001318","article-title":"Discovery and preclinical validation of drug indications using compendia of public gene expression data","volume":"3","author":"Sirota","year":"2011","journal-title":"Sci. Transl. Med"},{"key":"2023020108471601400_btab035-B29","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/nprot.2011.457","article-title":"Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses","volume":"7","author":"Stegle","year":"2012","journal-title":"Nat. Protoc"},{"key":"2023020108471601400_btab035-B30","first-page":"26","article-title":"Lecture 6.5\u2014rmsprop: divide the radient by a running average of its recent magnitude","volume":"4","author":"Tieleman","year":"2012","journal-title":"COURSERA Neural Netw. Mach. Learn"},{"key":"2023020108471601400_btab035-B31","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/1471-2105-7-43","article-title":"SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms","volume":"7","author":"Van den Bulcke","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023020108471601400_btab035-B32","first-page":"3835","author":"Virmaux","year":"2018"},{"key":"2023020108471601400_btab035-B33","doi-asserted-by":"crossref","first-page":"180061","DOI":"10.1038\/sdata.2018.61","article-title":"Unifying cancer and normal RNA sequencing data from different sources","volume":"5","author":"Wang","year":"2018","journal-title":"Sci. Data"},{"key":"2023020108471601400_btab035-B34","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The Cancer Genome Atlas Pan-Cancer analysis project","volume":"45","author":"Weinstein","year":"2013","journal-title":"Nat. Genet"},{"key":"2023020108471601400_btab035-B35","doi-asserted-by":"crossref","first-page":"3594","DOI":"10.1093\/bioinformatics\/bth448","article-title":"Advances to Bayesian network inference for generating causal networks from observational biological data","volume":"20","author":"Yu","year":"2004","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab035\/36201246\/btab035.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/3\/730\/49008453\/btab035.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/3\/730\/49008453\/btab035.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T15:04:01Z","timestamp":1675263841000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/3\/730\/6104825"}},"subtitle":[],"editor":[{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1,20]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,1,12]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab035","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/836254","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,2,1]]},"published":{"date-parts":[[2021,1,20]]}}}