scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis.
With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for compr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012679 |
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| _version_ | 1850250845977837568 |
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| author | Wei Lan Tongsheng Ling Qingfeng Chen Ruiqing Zheng Min Li Yi Pan |
| author_facet | Wei Lan Tongsheng Ling Qingfeng Chen Ruiqing Zheng Min Li Yi Pan |
| author_sort | Wei Lan |
| collection | DOAJ |
| description | With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data. |
| format | Article |
| id | doaj-art-526db3a843574bcea4d96a080e63b73f |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-526db3a843574bcea4d96a080e63b73f2025-08-20T01:58:04ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101267910.1371/journal.pcbi.1012679scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis.Wei LanTongsheng LingQingfeng ChenRuiqing ZhengMin LiYi PanWith the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data.https://doi.org/10.1371/journal.pcbi.1012679 |
| spellingShingle | Wei Lan Tongsheng Ling Qingfeng Chen Ruiqing Zheng Min Li Yi Pan scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis. PLoS Computational Biology |
| title | scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis. |
| title_full | scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis. |
| title_fullStr | scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis. |
| title_full_unstemmed | scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis. |
| title_short | scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis. |
| title_sort | scmomtf an interpretable multitask learning framework for single cell multi omics data analysis |
| url | https://doi.org/10.1371/journal.pcbi.1012679 |
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