Journal
GENOME BIOLOGY
Volume 23, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13059-022-02663-5
Keywords
Spatial omics; Multiplexed data; Machine learning; Intercellular signaling
Funding
- European Union
- Slovenian Ministry of Education, Science and Sport [C3330-17-529021]
- Early Postdoc Mobility fellowship [P2ZHP3_181475]
- Damon Runyon Cancer Research Foundation [DRQ-03-20]
- German Federal Ministry of Education and Research [BMBF 01ZZ2004]
- Projekt DEAL
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The paper presents MISTy, a machine learning framework that can extract relationships from any spatial omics data. The framework is flexible, scalable, and capable of dissecting different effects through the construction of multiple views.
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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