RT Journal Article
SR Electronic
T1 A generalization of t-SNE and UMAP to single-cell multimodal omics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.01.10.426098
DO 10.1101/2021.01.10.426098
A1 Van Hoan Do
A1 Stefan Canzar
YR 2021
UL http://biorxiv.org/content/early/2021/01/10/2021.01.10.426098.abstract
AB Emerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes. j-SNE and j-UMAP are available in the JVis Python package.Competing Interest StatementThe authors have declared no competing interest.