Paired single-cell multi-omics data integration with Mowgli
Abstract The profiling of multiple molecular layers from the same set of cells has recently become possible. There is thus a growing need for multi-view learning methods able to jointly analyze these data. We here present Multi-Omics Wasserstein inteGrative anaLysIs (Mowgli), a novel method for the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-43019-2 |
| _version_ | 1797452078753251328 |
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| author | Geert-Jan Huizing Ina Maria Deutschmann Gabriel Peyré Laura Cantini |
| author_facet | Geert-Jan Huizing Ina Maria Deutschmann Gabriel Peyré Laura Cantini |
| author_sort | Geert-Jan Huizing |
| collection | DOAJ |
| description | Abstract The profiling of multiple molecular layers from the same set of cells has recently become possible. There is thus a growing need for multi-view learning methods able to jointly analyze these data. We here present Multi-Omics Wasserstein inteGrative anaLysIs (Mowgli), a novel method for the integration of paired multi-omics data with any type and number of omics. Of note, Mowgli combines integrative Nonnegative Matrix Factorization and Optimal Transport, enhancing at the same time the clustering performance and interpretability of integrative Nonnegative Matrix Factorization. We apply Mowgli to multiple paired single-cell multi-omics data profiled with 10X Multiome, CITE-seq, and TEA-seq. Our in-depth benchmark demonstrates that Mowgli’s performance is competitive with the state-of-the-art in cell clustering and superior to the state-of-the-art once considering biological interpretability. Mowgli is implemented as a Python package seamlessly integrated within the scverse ecosystem and it is available at http://github.com/cantinilab/mowgli . |
| first_indexed | 2024-03-09T15:03:34Z |
| format | Article |
| id | doaj.art-058da6336aa34501a7f2a3bee84f345e |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-09T15:03:34Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-058da6336aa34501a7f2a3bee84f345e2023-11-26T13:45:01ZengNature PortfolioNature Communications2041-17232023-11-0114111310.1038/s41467-023-43019-2Paired single-cell multi-omics data integration with MowgliGeert-Jan Huizing0Ina Maria Deutschmann1Gabriel Peyré2Laura Cantini3Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics GroupInstitut de Biologie de l’Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSLCNRS and DMA de l’Ecole Normale Supérieure, CNRS, Ecole Normale Supérieure, Université PSLInstitut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics GroupAbstract The profiling of multiple molecular layers from the same set of cells has recently become possible. There is thus a growing need for multi-view learning methods able to jointly analyze these data. We here present Multi-Omics Wasserstein inteGrative anaLysIs (Mowgli), a novel method for the integration of paired multi-omics data with any type and number of omics. Of note, Mowgli combines integrative Nonnegative Matrix Factorization and Optimal Transport, enhancing at the same time the clustering performance and interpretability of integrative Nonnegative Matrix Factorization. We apply Mowgli to multiple paired single-cell multi-omics data profiled with 10X Multiome, CITE-seq, and TEA-seq. Our in-depth benchmark demonstrates that Mowgli’s performance is competitive with the state-of-the-art in cell clustering and superior to the state-of-the-art once considering biological interpretability. Mowgli is implemented as a Python package seamlessly integrated within the scverse ecosystem and it is available at http://github.com/cantinilab/mowgli .https://doi.org/10.1038/s41467-023-43019-2 |
| spellingShingle | Geert-Jan Huizing Ina Maria Deutschmann Gabriel Peyré Laura Cantini Paired single-cell multi-omics data integration with Mowgli Nature Communications |
| title | Paired single-cell multi-omics data integration with Mowgli |
| title_full | Paired single-cell multi-omics data integration with Mowgli |
| title_fullStr | Paired single-cell multi-omics data integration with Mowgli |
| title_full_unstemmed | Paired single-cell multi-omics data integration with Mowgli |
| title_short | Paired single-cell multi-omics data integration with Mowgli |
| title_sort | paired single cell multi omics data integration with mowgli |
| url | https://doi.org/10.1038/s41467-023-43019-2 |
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