论文标题
回顾单细胞RNA-seq数据聚类，以识别细胞类型和表征
Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization
论文作者
论文摘要
近年来，单细胞RNA-seq技术的进步使我们能够以高通量方式以单细胞分辨率进行大规模的转录组分析。无监督的学习（例如数据聚类）已成为识别和表征新型细胞类型和基因表达模式的中心成分。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，并具有对相关优势和局限性的重要见解。此外，我们还回顾了上游单细胞RNA-seq数据处理技术，例如质量控制，归一化和降低尺寸。我们进行性能比较实验，以评估两个单细胞转录组数据集上的几种流行的单细胞RNA-seq聚类方法。
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 two single-cell transcriptomic datasets.
