MultiVI: deep generative model for the integration of multi-modal data
MultiVI: deep generative model for the integration of multi-modal data
Abstract Jointly profiling the transcriptional and chromatin accessibility landscapes of single-cells is a powerful technique to characterize cellular populations. Here we present MultiVI, a probabilistic model to analyze such multiomic data and integrate it with single modality datasets. MultiVI creates a joint representation that accurately reflects both chromatin and transcriptional properties of the cells even when one modality is missing. It also imputes missing data, corrects for batch effects and is available in the scvi-tools framework: https://docs.scvi-tools.org/.
Ashuach Tal、Yosef Nir、Jordan Michael I.、Gabitto Mariano I.
Center for Computational Biology, University of California||Department of Electrical Engineering and Computer Sciences, University of CaliforniaCenter for Computational Biology, University of California||Department of Electrical Engineering and Computer Sciences, University of California||Ragon Institute of MGH, MIT, and Harvard||Chan Zuckerberg BioHubDepartment of Electrical Engineering and Computer Sciences, University of California||Department of Statistics, University of CaliforniaDepartment of Electrical Engineering and Computer Sciences, University of California||Department of Statistics, University of California||Allen Institute for Brain Science
细胞生物学分子生物学生物科学研究方法、生物科学研究技术
Ashuach Tal,Yosef Nir,Jordan Michael I.,Gabitto Mariano I..MultiVI: deep generative model for the integration of multi-modal data[EB/OL].(2025-03-28)[2025-06-04].https://www.biorxiv.org/content/10.1101/2021.08.20.457057.点此复制
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