| Autor: |
Stefan Canzar, Van Hoan Do, Slobodan Jelić, Sören Laue, Domagoj Matijević, Tomislav Prusina |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Algorithms for Molecular Biology, Vol 19, Iss 1, Pp 1-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1748-7188 |
| DOI: |
10.1186/s13015-024-00265-3 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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