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VI-VS: calibrated identification of feature dependencies in single-cell multiomics

Author(s)
Boyeau, Pierre; Bates, Stephen; Ergen, Can; Jordan, Michael I.; Yosef, Nir
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Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/
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Abstract
Unveiling functional relationships between various molecular cell phenotypes from data using machine learning models is a key promise of multiomics. Existing methods either use flexible but hard-to-interpret models or simpler, misspecified models. VI-VS (Variational Inference for Variable Selection) balances flexibility and interpretability to identify relevant feature relationships in multiomic data. It uses deep generative models to identify conditionally dependent features, with false discovery rate control. VI-VS is available as an open-source Python package, providing a robust solution to identify features more likely representing genuine causal relationships.
Date issued
2024-11-15
URI
https://hdl.handle.net/1721.1/157562
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Genome Biology
Publisher
BioMed Central
Citation
Boyeau, P., Bates, S., Ergen, C. et al. VI-VS: calibrated identification of feature dependencies in single-cell multiomics. Genome Biol 25, 294 (2024).
Version: Final published version

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