Explainable multiview framework for dissecting spatial relationships from highly multiplexed data
The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measure...
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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
14 April 2022
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| In: |
Genome biology
Year: 2022, Jahrgang: 23, Pages: 1-31 |
| ISSN: | 1474-760X |
| DOI: | 10.1186/s13059-022-02663-5 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s13059-022-02663-5
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| Verfasserangaben: | Jovan Tanevski, Ricardo Omar Ramirez Flores, Attila Gabor, Denis Schapiro and Julio Saez-Rodriguez |
| Zusammenfassung: | The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interactions coming from different spatial contexts in breast cancer and demonstrated how to relate MISTy’s results to clinical features. |
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| Beschreibung: | Gesehen am 17.05.2022 |
| Beschreibung: | Online Resource |
| ISSN: | 1474-760X |
| DOI: | 10.1186/s13059-022-02663-5 |