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Meanwhile, size, sparsity and high dimensionality of the resulting data continue to pose challenges for its computational analysis, and specifically the integration of data from different sources. We have developed a dedicated computational approach: a variational auto-encoder using a noise model specifically designed for single-cell ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) data, which facilitates simultaneous dimensionality reduction and batch correction via an adversarial learning strategy. We showcase its benefits for detailed cell-type characterization on individual real and simulated datasets as well as for integrating multiple complex datasets.<\/jats:p>","DOI":"10.1038\/s42256-022-00443-1","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T12:04:19Z","timestamp":1645617859000},"page":"162-168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0376-0032","authenticated-orcid":false,"given":"Wolfgang","family":"Kopp","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0468-0117","authenticated-orcid":false,"given":"Altuna","family":"Akalin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0881-3116","authenticated-orcid":false,"given":"Uwe","family":"Ohler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"443_CR1","doi-asserted-by":"crossref","unstructured":"Zamanighomi, M. et al. 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