{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:42:59Z","timestamp":1778755379965,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T00:00:00Z","timestamp":1582243200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T00:00:00Z","timestamp":1582243200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["Career award (grant 1651236)"],"award-info":[{"award-number":["Career award (grant 1651236)"]}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["R01HG008164"],"award-info":[{"award-number":["R01HG008164"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements).<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>To overcome these difficulties, we propose DR-A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction. DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of generative adversarial networks. DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DR-A is able to provide a more accurate low dimensional representation of the scRNA-seq data. We illustrate this by utilizing DR-A for clustering of scRNA-seq data.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-020-3401-5","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T15:02:44Z","timestamp":1582297364000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis"],"prefix":"10.1186","volume":"21","author":[{"given":"Eugene","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudipto","family":"Mukherjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sreeram","family":"Kannan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,21]]},"reference":[{"issue":"6226","key":"3401_CR1","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1126\/science.aaa1934","volume":"347","author":"A Zeisel","year":"2015","unstructured":"Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G, Jureus A, Marques S, Munguba H, He L, Betsholtz C, et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015;347(6226):1138\u201342.","journal-title":"Science"},{"issue":"13","key":"3401_CR2","doi-asserted-by":"publisher","first-page":"i124","DOI":"10.1093\/bioinformatics\/bty293","volume":"34","author":"S Mukherjee","year":"2018","unstructured":"Mukherjee S, Zhang Y, Fan J, Seelig G, Kannan S. Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge. Bioinformatics. 2018;34(13):i124\u201332.","journal-title":"Bioinformatics"},{"key":"3401_CR3","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1007\/978-3-642-04898-2_455","volume-title":"International Encyclopedia of Statistical Science","author":"Ian Jolliffe","year":"2011","unstructured":"Jolliffe I. Principal component analysis. In: International encyclopedia of statistical science: Berlin: Springer; 2011. p. 1094\u20136."},{"key":"3401_CR4","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.mam.2017.07.002","volume":"59","author":"TS Andrews","year":"2018","unstructured":"Andrews TS, Hemberg M. Identifying cell populations with scRNASeq. Mol Asp Med. 2018;59:114\u201322.","journal-title":"Mol Asp Med"},{"key":"3401_CR5","doi-asserted-by":"publisher","DOI":"10.4324\/9781315788135","volume-title":"An Easy Guide to Factor Analysis","author":"Paul Kline","year":"2014","unstructured":"Kline P. An easy guide to factor analysis: New York: Routledge; 2014."},{"key":"3401_CR6","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1186\/s13059-015-0805-z","volume":"16","author":"E Pierson","year":"2015","unstructured":"Pierson E, Yau C. ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 2015;16:241.","journal-title":"Genome Biol"},{"issue":"12","key":"3401_CR7","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","volume":"15","author":"R Lopez","year":"2018","unstructured":"Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018;15(12):1053.","journal-title":"Nat Methods"},{"issue":"11","key":"3401_CR8","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1038\/s41592-019-0576-7","volume":"16","author":"Matthew Amodio","year":"2019","unstructured":"Amodio M, Van Dijk D, Srinivasan K, Chen WS, Mohsen H, Moon KR, Campbell A, Zhao Y, Wang X, Venkataswamy M. Exploring single-cell data with deep multitasking neural networks. Nat Methods. 2019;16(11):1139\u201345.","journal-title":"Nature Methods"},{"issue":"3","key":"3401_CR9","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nn.4495","volume":"20","author":"JN Campbell","year":"2017","unstructured":"Campbell JN, Macosko EZ, Fenselau H, Pers TH, Lyubetskaya A, Tenen D, Goldman M, Verstegen AM, Resch JM, McCarroll SA, et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat Neurosci. 2017;20(3):484\u201396.","journal-title":"Nat Neurosci"},{"issue":"5","key":"3401_CR10","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","volume":"161","author":"EZ Macosko","year":"2015","unstructured":"Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202\u201314.","journal-title":"Cell"},{"issue":"4","key":"3401_CR11","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","volume":"3","author":"M Baron","year":"2016","unstructured":"Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM, et al. A single-cell Transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 2016;3(4):346\u201360 e344.","journal-title":"Cell Syst"},{"issue":"Nov","key":"3401_CR12","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(Nov):2579\u2013605.","journal-title":"J Mach Learn Res"},{"key":"3401_CR13","volume-title":"Umap: Uniform manifold approximation and projection for dimension reduction","author":"L McInnes","year":"2018","unstructured":"McInnes L, Healy J, Melville J: Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:180203426 2018."},{"issue":"1","key":"3401_CR14","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/nbt.4314","volume":"37","author":"E Becht","year":"2019","unstructured":"Becht E, McInnes L, Healy J, Dutertre C-A, Kwok IW, Ng LG, Ginhoux F, Newell EW. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2019;37(1):38.","journal-title":"Nat Biotechnol"},{"key":"3401_CR15","first-page":"2672","volume-title":"Advances in neural information processing systems","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Advances in neural information processing systems; 2014. p. 2672\u201380."},{"key":"3401_CR16","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.media.2018.07.001","volume":"49","author":"H Zhao","year":"2018","unstructured":"Zhao H, Li H, Maurer-Stroh S, Cheng L. Synthesizing retinal and neuronal images with generative adversarial nets. Med Image Anal. 2018;49:14\u201326.","journal-title":"Med Image Anal"},{"issue":"3","key":"3401_CR17","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1109\/JBHI.2018.2852639","volume":"23","author":"B Hu","year":"2018","unstructured":"Hu B, Tang Y, Chang EI, Fan Y, Lai M, Xu Y. Unsupervised learning for cell-level visual representation with generative adversarial networks. IEEE J Biomed Health Inform. 2018;23(3):1316\u201328.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"3401_CR18","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1109\/TMI.2018.2858752","volume":"38","author":"M Mardani","year":"2018","unstructured":"Mardani M, Gong E, Cheng JY, Vasanawala SS, Zaharchuk G, Xing L, Pauly JM. Deep generative adversarial neural networks for compressive sensing (GANCS) MRI. IEEE Trans Med Imaging. 2018;38(1):167\u201379.","journal-title":"IEEE Trans Med Imaging"},{"key":"3401_CR19","volume-title":"Adversarial autoencoders","author":"A Makhzani","year":"2015","unstructured":"Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B: Adversarial autoencoders. arXiv preprint arXiv:151105644 2015."},{"issue":"7","key":"3401_CR20","doi-asserted-by":"publisher","first-page":"10883","DOI":"10.18632\/oncotarget.14073","volume":"8","author":"A Kadurin","year":"2017","unstructured":"Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, Zhavoronkov A. The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017;8(7):10883\u201390.","journal-title":"Oncotarget"},{"issue":"9","key":"3401_CR21","doi-asserted-by":"publisher","first-page":"3098","DOI":"10.1021\/acs.molpharmaceut.7b00346","volume":"14","author":"A Kadurin","year":"2017","unstructured":"Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in Silico. Mol Pharm. 2017;14(9):3098\u2013104.","journal-title":"Mol Pharm"},{"key":"3401_CR22","volume-title":"Auto-encoding variational bayes","author":"DP Kingma","year":"2013","unstructured":"Kingma DP, Welling M: Auto-encoding variational bayes. arXiv preprint arXiv:13126114 2013."},{"issue":"6","key":"3401_CR23","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1038\/nmeth.2930","volume":"11","author":"D Gr\u00fcn","year":"2014","unstructured":"Gr\u00fcn D, Kester L, Van Oudenaarden A. Validation of noise models for single-cell transcriptomics. Nat Methods. 2014;11(6):637.","journal-title":"Nat Methods"},{"key":"3401_CR24","first-page":"5767","volume-title":"Advances in Neural Information Processing Systems","author":"I Gulrajani","year":"2017","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC. Improved training of wasserstein gans. In: Advances in Neural Information Processing Systems; 2017. p. 5767\u201377."},{"issue":"8","key":"3401_CR25","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1016\/S0031-3203(03)00035-9","volume":"36","author":"E Choi","year":"2003","unstructured":"Choi E, Lee C. Feature extraction based on the Bhattacharyya distance. Pattern Recogn. 2003;36(8):1703\u20139.","journal-title":"Pattern Recogn"},{"key":"3401_CR26","doi-asserted-by":"publisher","first-page":"14049","DOI":"10.1038\/ncomms14049","volume":"8","author":"GX Zheng","year":"2017","unstructured":"Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049.","journal-title":"Nat Commun"},{"issue":"6385","key":"3401_CR27","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1126\/science.aam8999","volume":"360","author":"AB Rosenberg","year":"2018","unstructured":"Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P, Yao Z, Graybuck LT, Peeler DJ, Mukherjee S, Chen W. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018;360(6385):176\u201382.","journal-title":"Science"},{"issue":"Dec","key":"3401_CR28","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl A, Ghosh J. Cluster ensembles---a knowledge reuse framework for combining multiple partitions. J Mach Learn Res. 2002;3(Dec):583\u2013617.","journal-title":"J Mach Learn Res"},{"key":"3401_CR29","volume-title":"Adam: A method for stochastic optimization","author":"DP Kingma","year":"2014","unstructured":"Kingma DP, Ba J: Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980 2014."},{"key":"3401_CR30","volume-title":"Wasserstein gan","author":"M Arjovsky","year":"2017","unstructured":"Arjovsky M, Chintala S, Bottou L: Wasserstein gan. arXiv preprint arXiv:170107875 2017."},{"issue":"1","key":"3401_CR31","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1038\/s41467-017-02554-5","volume":"9","author":"D Risso","year":"2018","unstructured":"Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun. 2018;9(1):284.","journal-title":"Nat Commun"},{"issue":"8","key":"3401_CR32","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798\u2013828.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3401_CR33","volume-title":"Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence","author":"S Mukherjee","year":"2018","unstructured":"Mukherjee S, Asnani H, Lin E, Kannan S. ClusterGAN: latent space clustering in generative adversarial networks. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence; 2018."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-3401-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-3401-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-3401-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T00:39:53Z","timestamp":1613867993000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-3401-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,21]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3401"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-3401-5","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,21]]},"assertion":[{"value":"20 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"64"}}