Canzar, Stefan and Do, Van Hoan and Jelic, Slobodan and Laue, Soeren and Matijevic, Domagoj and Prusina, Tomislav (2024) Metric multidimensional scaling for large single-cell datasets using neural networks. ALGORITHMS FOR MOLECULAR BIOLOGY, 19 (1): 21. ISSN 1748-7188
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | RNA-SEQ; Metric multidimensional scaling; Neural networks; Large-scale data; Dimensionality reduction; Single-cell RNA-seq; Clustering |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Computational Life Science > Algorithmische Bioinformatik (Prof. Dr. Stefan Canzar) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 30 Oct 2025 07:05 |
| Last Modified: | 30 Oct 2025 07:05 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64139 |
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