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Review of single-cell RNA-seq data clustering for cell-type identification and characterization
Shixiong Zhang1,2, Xiangtao Li3, Jiecong Lin2
1School of Computer Science and Technology, Xidian University, Xi'an 710071, China sxzhang7-c@my.cityu.edu.hk.
RNA (New York, N.Y.)
|February 3, 2023
Summary
This study reviews single-cell RNA sequencing (scRNA-seq) data clustering methods and upstream processing techniques. It evaluates popular clustering approaches to identify novel cell types and gene expression patterns.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution.
- Unsupervised learning, particularly data clustering, is crucial for identifying novel cell types and gene expression patterns in scRNA-seq data.
Conclusions:
- Effective clustering and preprocessing are vital for accurate cell type identification from scRNA-seq data.
- The choice of clustering method and preprocessing pipeline significantly impacts downstream biological interpretation.
- This review provides a critical resource for researchers selecting and applying scRNA-seq analysis methods.
