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It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a simple neural network-based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.<\/jats:p>","DOI":"10.1186\/s13015-024-00265-3","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T11:14:21Z","timestamp":1718104461000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Metric multidimensional scaling for large single-cell datasets using neural networks"],"prefix":"10.1186","volume":"19","author":[{"given":"Stefan","family":"Canzar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Van Hoan","family":"Do","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Slobodan","family":"Jeli\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00f6ren","family":"Laue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Domagoj","family":"Matijevi\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomislav","family":"Prusina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,11]]},"reference":[{"issue":"1","key":"265_CR1","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1186\/s13059-019-1898-6","volume":"20","author":"S Sun","year":"2019","unstructured":"Sun S, Zhu J, Ma Y, Zhou X. 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