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Updated: May 8, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics

Yidi Sun¹, Lingling Kong¹, Jiayi Huang¹

¹School of Computer Science and Technology, Hainan University, Haikou 570228, China.

Briefings in Functional Genomics

|June 11, 2024

Summary

This review summarizes essential dimensionality reduction and clustering algorithms for single-cell and spatial transcriptomic data analysis. These methods help visualize and group cells, revealing cellular diversity and guiding further research.

Keywords:

ScRNA-seq clustering dimensionality reduction spatial transcriptomics

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell transcriptomics and spatial transcriptomics are powerful tools for biological research.
Analyzing this data requires dimensionality reduction and clustering due to its high-dimensional nature.
These techniques are crucial for visualizing data and identifying distinct cell populations.

Purpose of the Study:

To systematically review widely recognized algorithms for dimensionality reduction and clustering.
To provide insights for developing novel analytical tools for transcriptomic data.
To aid researchers in analyzing and interpreting single-cell and spatial transcriptomic datasets.

Main Methods:

Review of established algorithms for dimensionality reduction (e.g., PCA, t-SNE, UMAP).
Review of established algorithms for clustering (e.g., k-means, hierarchical clustering, graph-based clustering).
Focus on applications in single-cell and spatial transcriptomic data analysis.

Main Results:

Identification of key algorithms commonly used for transcriptomic data analysis.
Demonstration of how dimensionality reduction aids in data visualization and relationship observation.
Explanation of how clustering facilitates the identification of cell subpopulations and cellular diversity.

Conclusions:

Dimensionality reduction and clustering are indispensable for analyzing high-dimensional transcriptomic data.
A systematic summary of algorithms provides valuable guidance for researchers.
This review contributes to the advancement of computational tools in transcriptomics.