Review of single-cell RNA-seq data clustering for cell-type identification and characterization

In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.

Automated summary

The advances in single-cell RNA-seq techniques have allowed for large-scale transcriptomic profiling at single-cell resolution. Unsupervised learning, such as data clustering, is a key component in identifying and characterizing novel cell types and gene expression patterns. This study reviews existing single-cell RNA-seq data clustering methods, including their advantages and limitations, as well as upstream data processing techniques like quality control, normalization, and dimension reduction. Performance comparison experiments evaluate popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic datasets.