RT Journal Article
SR Electronic
T1 Metric Multidimensional Scaling for Large Single-Cell Data Sets using Neural Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.06.24.449725
DO 10.1101/2021.06.24.449725
A1 Stefan Canzar
A1 Van Hoan Do
A1 Slobodan Jelić
A1 Sören Laue
A1 Domagoj Matijević
A1 Tomislav Prusina
YR 2021
UL http://biorxiv.org/content/early/2021/06/25/2021.06.24.449725.abstract
AB 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 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.Competing Interest StatementThe authors have declared no competing interest.