MultiVI: deep generative model for the integration of multimodal data

被引：0
|
作者：
Tal Ashuach
Mariano I. Gabitto
Rohan V. Koodli
Giuseppe-Antonio Saldi
Michael I. Jordan
Nir Yosef
机构：
[1] University of California,Center for Computational Biology
[2] University of California,Department of Electrical Engineering and Computer Sciences
[3] University of California,Department of Statistics
[4] Berkeley,Department of Systems Immunology
[5] Allen Institute for Brain Science,undefined
[6] Weizmann Institute of Science,undefined
来源：
Nature Methods | 2023年 / 20卷
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摘要：
Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data, even for cells for which one or more modalities are missing. It is available at scvi-tools.org.
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页码：1222 / 1231
页数：9
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