Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization

• URL: http://arxiv.org/abs/2001.01006v1
• Date: Fri, 3 Jan 2020 22:48:10 GMT
• Title: Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization
• Authors: Shixiong Zhang, Xiangtao Li, Qiuzhen Lin, and Ka-Chun Wong
• Abstract summary: Unsupervised learning has become the central component to identify and characterize novel cell types and gene expression patterns. We review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on two single-cell transcriptomic datasets.
• Score: 12.655970720359297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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