A generalization of t-SNE and UMAP to single-cell multimodal omics

A generalization of t-SNE and UMAP to single-cell multimodal omics

Abstract 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 t...

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Bibliographic Details
Main Authors: Van Hoan Do, Stefan Canzar
Format: Article
Language:English
Published: BMC 2021-05-01
Series:Genome Biology
Subjects:
Data visualization
Single-cell sequencing
Multimodal omics
t-SNE
UMAP
RNA velocity
Online Access:https://doi.org/10.1186/s13059-021-02356-5
Description
Summary:Abstract 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.
ISSN:1474-760X

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