School of Computer Science and Technology, Xidian University, Xi'an 710071, China
Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China.
RNA. 2023 May;29(5):517-530. doi: 10.1261/rna.078965.121. Epub 2023 Feb 3.
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.
近年来，单细胞 RNA-seq 技术的进步使我们能够以高通量的方式在单细胞分辨率下进行大规模转录组分析。无监督学习，如数据聚类，已成为识别和描述新型细胞类型和基因表达模式的核心组成部分。在这项研究中，我们回顾了现有的单细胞 RNA-seq 数据聚类方法，并对相关的优缺点进行了批判性的分析。此外，我们还回顾了上游的单细胞 RNA-seq 数据处理技术，如质量控制、归一化和降维。我们进行了性能比较实验，以评估几种流行的单细胞 RNA-seq 聚类方法在模拟和多个单细胞转录组数据集上的性能。
