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| Verfasst von: | Rahimi, Arezou [VerfasserIn]  |
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| | Vale Silva, Luis A. [VerfasserIn] |
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| | Fälth Savitski, Maria [VerfasserIn] |
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| | Tanevski, Jovan [VerfasserIn] |
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| | Sáez Rodríguez, Julio [VerfasserIn] |
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| Titel: | DOT |
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| Titelzusatz: | a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics |
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| Verf.angabe: | Arezou Rahimi, Luis A. Vale-Silva, Maria Fälth Savitski, Jovan Tanevski & Julio Saez-Rodriguez |
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| E-Jahr: | 2024 |
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| Jahr: | 11 June 2024 |
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| Umfang: | 15 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 20.01.2025 |
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| Titel Quelle: | Enthalten in: Nature Communications |
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| Ort Quelle: | [London] : Springer Nature, 2010 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 15(2024), Artikel-ID 4994, Seite 1-15 |
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| ISSN Quelle: | 2041-1723 |
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| Abstract: | Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information. DOT uses genes beyond those common to the data modalities, exploits the local spatial context, transfers spatial features beyond cell-type information, and infers absolute/relative abundance of cell populations at tissue locations. Thus, DOT bridges single-cell transcriptomics data with both high- and low-resolution spatially-resolved data. Moreover, DOT combines practical aspects related to cell composition, heterogeneity, technical effects, and integration of prior knowledge. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data. |
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| DOI: | doi:10.1038/s41467-024-48868-z |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1038/s41467-024-48868-z |
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| | kostenfrei: Volltext: https://www.nature.com/articles/s41467-024-48868-z |
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| | DOI: https://doi.org/10.1038/s41467-024-48868-z |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Biotechnology |
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| | Computational models |
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| | Computational science |
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| | Data integration |
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| | Machine learning |
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| K10plus-PPN: | 1915136202 |
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| Verknüpfungen: | → Zeitschrift |
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