2020/01/03 by Shixiong Zhang, Xiangtao Li, Zhang, Shixiong +5 · 2 citations
Biochemistry, Genetics and Molecular Biology · #Extracellular vesicles in disease #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM) #Single-cell and spatial transcriptomics
paper · pdf · doi:10.48550/arxiv.2001.01006
openalex publication_date 2020/01/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.