M-band wavelet-based multi-view clustering of cells
Guardado en:
| Publicado en: | PLoS Computational Biology vol. 21, no. 5 (May 2025), p. e1013060-e1013076 |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
Public Library of Science
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3270867506 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1553-734X | ||
| 022 | |a 1553-7358 | ||
| 024 | 7 | |a 10.1371/journal.pcbi.1013060 |2 doi | |
| 035 | |a 3270867506 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 084 | |a 174831 |2 nlm | ||
| 100 | 1 | |a Liu, Tong | |
| 245 | 1 | |a M-band wavelet-based multi-view clustering of cells | |
| 260 | |b Public Library of Science |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Signal processing | ||
| 653 | |a Data processing | ||
| 653 | |a Data analysis | ||
| 653 | |a Cells | ||
| 653 | |a Datasets | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Gene sequencing | ||
| 653 | |a Clustering | ||
| 653 | |a Quality control | ||
| 653 | |a Decomposition | ||
| 653 | |a Approximation | ||
| 653 | |a Methods | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Colorectal cancer | ||
| 653 | |a Genomics | ||
| 653 | |a Genes | ||
| 653 | |a Visualization | ||
| 653 | |a Wavelet analysis | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Liu, Zihuan | |
| 700 | 1 | |a Sun, Wenke | |
| 700 | 1 | |a Shankar, Adeethyia | |
| 700 | 1 | |a Zhao, Yongzhong | |
| 700 | 1 | |a Wang, Xiaodi | |
| 773 | 0 | |t PLoS Computational Biology |g vol. 21, no. 5 (May 2025), p. e1013060-e1013076 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3270867506/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3270867506/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3270867506/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |