Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Cell clustering is crucial for understanding cellular architecture in single-cell RNA sequencing (scRNA-seq).
• Current scRNA-seq clustering methods struggle to resolve complex cellular landscapes at high resolution.

Purpose of the Study:

• To develop a novel multiscale clustering (MSC) approach for scRNA-seq data analysis.
• To enable unsupervised identification of cell types and subtypes across multiple resolutions.

Main Methods:

• Constructed a sparse cell-cell correlation network.
• Applied the multiscale clustering (MSC) approach to simulated and real scRNA-seq datasets.

Main Results:

• MSC demonstrated superior performance compared to benchmark methods on simulated and real disease data.
• Identified a biologically meaningful cell hierarchy, revealing novel disease-associated cell subtypes.

Conclusions:

• MSC offers a powerful new framework for single-cell multiscale clustering.
• This approach advances the discovery of disease-associated cell populations using scRNA-seq data.