MultiVI: deep generative model for the integration of multimodal data

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 cr...

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Detalles Bibliográficos
Autores principales: Ashuach, Tal, Gabitto, Mariano I., Koodli, Rohan V., Saldi, Giuseppe-Antonio, Jordan, Michael I., Yosef, Nir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406609/
https://www.ncbi.nlm.nih.gov/pubmed/37386189
http://dx.doi.org/10.1038/s41592-023-01909-9
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author Ashuach, Tal
Gabitto, Mariano I.
Koodli, Rohan V.
Saldi, Giuseppe-Antonio
Jordan, Michael I.
Yosef, Nir
author_facet Ashuach, Tal
Gabitto, Mariano I.
Koodli, Rohan V.
Saldi, Giuseppe-Antonio
Jordan, Michael I.
Yosef, Nir
author_sort Ashuach, Tal
collection PubMed
description 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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spelling pubmed-104066092023-08-09 MultiVI: deep generative model for the integration of multimodal data Ashuach, Tal Gabitto, Mariano I. Koodli, Rohan V. Saldi, Giuseppe-Antonio Jordan, Michael I. Yosef, Nir Nat Methods Article 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. Nature Publishing Group US 2023-06-29 2023 /pmc/articles/PMC10406609/ /pubmed/37386189 http://dx.doi.org/10.1038/s41592-023-01909-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ashuach, Tal
Gabitto, Mariano I.
Koodli, Rohan V.
Saldi, Giuseppe-Antonio
Jordan, Michael I.
Yosef, Nir
MultiVI: deep generative model for the integration of multimodal data
title MultiVI: deep generative model for the integration of multimodal data
title_full MultiVI: deep generative model for the integration of multimodal data
title_fullStr MultiVI: deep generative model for the integration of multimodal data
title_full_unstemmed MultiVI: deep generative model for the integration of multimodal data
title_short MultiVI: deep generative model for the integration of multimodal data
title_sort multivi: deep generative model for the integration of multimodal data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406609/
https://www.ncbi.nlm.nih.gov/pubmed/37386189
http://dx.doi.org/10.1038/s41592-023-01909-9
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