A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching
Abstract The integrated analysis of multiple gene expression profiles previously measured in distinct studies is problematic since missing both sample matches and common labels prevent their integration in fully data-driven, unsupervised training. In this study, we propose a strategy to enable the i...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-25524-4 |
| _version_ | 1811203725570605056 |
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| author | Y-h. Taguchi Turki Turki |
| author_facet | Y-h. Taguchi Turki Turki |
| author_sort | Y-h. Taguchi |
| collection | DOAJ |
| description | Abstract The integrated analysis of multiple gene expression profiles previously measured in distinct studies is problematic since missing both sample matches and common labels prevent their integration in fully data-driven, unsupervised training. In this study, we propose a strategy to enable the integration of multiple gene expression profiles among multiple independent studies with neither labeling nor sample matching using tensor decomposition unsupervised feature extraction. We apply this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack precise correspondence among samples, including AD single-cell RNA sequence (scRNA-seq) data. We were able to select biologically reasonable genes using the integrated analysis. Overall, integrated gene expression profiles can function analogously to prior- and/or transfer-learning strategies in other machine-learning applications. For scRNA-seq, the proposed approach significantly reduces the required computational memory. |
| first_indexed | 2024-04-12T02:59:47Z |
| format | Article |
| id | doaj.art-b56f2023d54a4d6d9f89a800aeb113a8 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T02:59:47Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b56f2023d54a4d6d9f89a800aeb113a82022-12-22T03:50:41ZengNature PortfolioScientific Reports2045-23222022-12-0112111110.1038/s41598-022-25524-4A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matchingY-h. Taguchi0Turki Turki1Department of Physics, Chuo UniversityDepartment of Computer Science, King Abdulaziz UniversityAbstract The integrated analysis of multiple gene expression profiles previously measured in distinct studies is problematic since missing both sample matches and common labels prevent their integration in fully data-driven, unsupervised training. In this study, we propose a strategy to enable the integration of multiple gene expression profiles among multiple independent studies with neither labeling nor sample matching using tensor decomposition unsupervised feature extraction. We apply this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack precise correspondence among samples, including AD single-cell RNA sequence (scRNA-seq) data. We were able to select biologically reasonable genes using the integrated analysis. Overall, integrated gene expression profiles can function analogously to prior- and/or transfer-learning strategies in other machine-learning applications. For scRNA-seq, the proposed approach significantly reduces the required computational memory.https://doi.org/10.1038/s41598-022-25524-4 |
| spellingShingle | Y-h. Taguchi Turki Turki A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching Scientific Reports |
| title | A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching |
| title_full | A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching |
| title_fullStr | A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching |
| title_full_unstemmed | A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching |
| title_short | A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching |
| title_sort | tensor decomposition based integrated analysis applicable to multiple gene expression profiles without sample matching |
| url | https://doi.org/10.1038/s41598-022-25524-4 |
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