Metric multidimensional scaling for large single-cell datasets using neural networks:Algorithms for Molecular Biology

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2024

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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 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. © The Author(s) 2024.

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Quelldatensatz: oai:www.edit.fis.uni-hamburg.de:publications/ffa923cf-74d2-48fd-9ddd-fa3c72ceba85