1. Metric multidimensional scaling for large single-cell datasets using neural networks
- Author
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Stefan Canzar, Van Hoan Do, Slobodan Jelić, Sören Laue, Domagoj Matijević, and Tomislav Prusina
- Subjects
Metric multidimensional scaling ,Neural networks ,Large-scale data ,Dimensionality reduction ,Single-cell RNA-seq ,Clustering ,Biology (General) ,QH301-705.5 ,Genetics ,QH426-470 - Abstract
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 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.
- Published
- 2024
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