  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.
