Metric multidimensional scaling for large single-cell datasets using neural networks.
Algorithms Mol Biol
; 19(1): 21, 2024 Jun 11.
Article
en En
| MEDLINE
| ID: mdl-38863064
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Algorithms Mol Biol
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania
Pais de publicación:
Reino Unido