Metric multidimensional scaling for large single-cell datasets using neural networks

URN to cite this document:: urn:nbn:de:bvb:355-epub-584242
DOI to cite this document:: 10.5283/epub.58424

Canzar, Stefan ; Do, Van Hoan ; Jelić, Slobodan ; Laue, Sören ; Matijević, Domagoj ; Prusina, Tomislav

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 18 Jun 2024 06:21

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Abstract

Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. 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 ...