Imagen de Portada

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: Liu, Tong
Otros Autores: Liu, Zihuan, Sun, Wenke, Shankar, Adeethyia, Zhao, Yongzhong, Wang, Xiaodi
Publicado:
Public Library of Science
Materias:
Signal processing
Data processing
Data analysis
Cells
Datasets
Wavelet transforms
Gene sequencing
Clustering
Quality control
Decomposition
Approximation
Methods
Cluster analysis
Colorectal cancer
Genomics
Genes
Visualization
Wavelet analysis
Environmental
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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.
ISSN:1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1013060
Fuente:Health & Medical Collection