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.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Chemie und Pharmazie > Department Biochemie |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 540 Chemie |
ISSN: | 1474-760X |
Sprache: | Englisch |
Dokumenten ID: | 97758 |
Datum der Veröffentlichung auf Open Access LMU: | 05. Jun. 2023, 15:27 |
Letzte Änderungen: | 05. Jun. 2023, 15:27 |