Review of single-cell RNA-seq data clustering for cell-type identification and characterization

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

8 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Pages (from-to)517-530
Journal / PublicationRNA
Issue number5
Online published3 Feb 2023
Publication statusPublished - May 2023



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. © 2023 Zhang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Research Area(s)

  • cell types, clustering, single-cell RNA-seq

Citation Format(s)

Download Statistics

No data available