M-band wavelet-based multi-view clustering of cells.
Wavelet analysis has been recognized as a widely used and promising tool in the fields of signal processing and data analysis. However, the application of wavelet-based method in single-cell RNA sequencing (scRNA-seq) data is little known. Here, we present M-band wavelet-based scRNA-seq multi-view c...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1013060 |
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| _version_ | 1850127303104790528 |
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| author | Tong Liu Zihuan Liu Wenke Sun Adeethyia Shankar Yongzhong Zhao Xiaodi Wang |
| author_facet | Tong Liu Zihuan Liu Wenke Sun Adeethyia Shankar Yongzhong Zhao Xiaodi Wang |
| author_sort | Tong Liu |
| collection | DOAJ |
| description | Wavelet analysis has been recognized as a widely used and promising tool in the fields of signal processing and data analysis. However, the application of wavelet-based method in single-cell RNA sequencing (scRNA-seq) data is little known. Here, we present M-band wavelet-based scRNA-seq multi-view clustering of cells (WMC). We applied for integration of M-band wavelet analysis and uniform manifold approximation and projection (UMAP) to a panel of single cell sequencing datasets by breaking up the data matrix into an approximation or low resolution component and M-1 detail or high resolution components. Our method is armed with multi-view clustering of cell types, identity, and functional states, enabling missing cell types visualization and new cell types discovery. Distinct to standard scRNA-seq workflow, our wavelet-based approach is a new addition to uncover rare cell types with a fine resolution. |
| format | Article |
| id | doaj-art-488af92e280d4362a08faed3375bb52e |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-488af92e280d4362a08faed3375bb52e2025-08-20T02:33:43ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-05-01215e101306010.1371/journal.pcbi.1013060M-band wavelet-based multi-view clustering of cells.Tong LiuZihuan LiuWenke SunAdeethyia ShankarYongzhong ZhaoXiaodi WangWavelet analysis has been recognized as a widely used and promising tool in the fields of signal processing and data analysis. However, the application of wavelet-based method in single-cell RNA sequencing (scRNA-seq) data is little known. Here, we present M-band wavelet-based scRNA-seq multi-view clustering of cells (WMC). We applied for integration of M-band wavelet analysis and uniform manifold approximation and projection (UMAP) to a panel of single cell sequencing datasets by breaking up the data matrix into an approximation or low resolution component and M-1 detail or high resolution components. Our method is armed with multi-view clustering of cell types, identity, and functional states, enabling missing cell types visualization and new cell types discovery. Distinct to standard scRNA-seq workflow, our wavelet-based approach is a new addition to uncover rare cell types with a fine resolution.https://doi.org/10.1371/journal.pcbi.1013060 |
| spellingShingle | Tong Liu Zihuan Liu Wenke Sun Adeethyia Shankar Yongzhong Zhao Xiaodi Wang M-band wavelet-based multi-view clustering of cells. PLoS Computational Biology |
| title | M-band wavelet-based multi-view clustering of cells. |
| title_full | M-band wavelet-based multi-view clustering of cells. |
| title_fullStr | M-band wavelet-based multi-view clustering of cells. |
| title_full_unstemmed | M-band wavelet-based multi-view clustering of cells. |
| title_short | M-band wavelet-based multi-view clustering of cells. |
| title_sort | m band wavelet based multi view clustering of cells |
| url | https://doi.org/10.1371/journal.pcbi.1013060 |
| work_keys_str_mv | AT tongliu mbandwaveletbasedmultiviewclusteringofcells AT zihuanliu mbandwaveletbasedmultiviewclusteringofcells AT wenkesun mbandwaveletbasedmultiviewclusteringofcells AT adeethyiashankar mbandwaveletbasedmultiviewclusteringofcells AT yongzhongzhao mbandwaveletbasedmultiviewclusteringofcells AT xiaodiwang mbandwaveletbasedmultiviewclusteringofcells |