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

作者：Shixiong Zhang, Xiangtao Li, Jiecong Lin, Qiuzhen Lin, Ka‐Chun Wong · 发表于：RNA · 年份：2023 · DOI：10.1261/rna.078965.121 · 被引用次数：117 · 研究领域：Single-cell and spatial transcriptomics、Extracellular vesicles in disease、Gene expression and cancer classification

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.