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

arXiv: https://arxiv.org/abs/2001.01006 · HTML: https://mlsanity.com/paper/2001.01006/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis](https://mlsanity.com/paper/2509.25884/) (2025)
- [Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types](https://mlsanity.com/paper/2311.17104/) (2023)

## Abstract (arXiv)

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.