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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doaj-2a2b62b8572a4eb2953fc10359f622882021-05-09T11:45:10ZengBMCGenome Biology1474-760X2021-05-012211910.1186/s13059-021-02356-5A generalization of t-SNE and UMAP to single-cell multimodal omicsVan Hoan Do0Stefan Canzar1Gene Center, Ludwig-Maximilians-Universität MünchenGene Center, Ludwig-Maximilians-Universität MünchenAbstract 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.https://doi.org/10.1186/s13059-021-02356-5Data visualizationSingle-cell sequencingMultimodal omicst-SNEUMAPRNA velocity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Van Hoan Do Stefan Canzar |
spellingShingle |
Van Hoan Do Stefan Canzar A generalization of t-SNE and UMAP to single-cell multimodal omics Genome Biology Data visualization Single-cell sequencing Multimodal omics t-SNE UMAP RNA velocity |
author_facet |
Van Hoan Do Stefan Canzar |
author_sort |
Van Hoan Do |
title |
A generalization of t-SNE and UMAP to single-cell multimodal omics |
title_short |
A generalization of t-SNE and UMAP to single-cell multimodal omics |
title_full |
A generalization of t-SNE and UMAP to single-cell multimodal omics |
title_fullStr |
A generalization of t-SNE and UMAP to single-cell multimodal omics |
title_full_unstemmed |
A generalization of t-SNE and UMAP to single-cell multimodal omics |
title_sort |
generalization of t-sne and umap to single-cell multimodal omics |
publisher |
BMC |
series |
Genome Biology |
issn |
1474-760X |
publishDate |
2021-05-01 |
description |
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. |
topic |
Data visualization Single-cell sequencing Multimodal omics t-SNE UMAP RNA velocity |
url |
https://doi.org/10.1186/s13059-021-02356-5 |
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