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Open Access

A Data-Driven Clustering Recommendation Method for Single-Cell RNA-Sequencing Data

Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada

† Yu Tian and Ruiqing Zheng contribute equally to this paper.

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Abstract

Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it possible to solve biological problems at the single-cell resolution. One of the critical steps in cellular heterogeneity analysis is the cell type identification. Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters. Among all the methods, hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning, dropout imputation, and dimensionality reduction. In this study, we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions. The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension, while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension. Motivated by this finding, a new strategy, called QRS, is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not. Finally, a data-driven clustering recommendation method, called DDCR, is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data. We perform DDCR on two typical single cell clustering methods, SC3 and RAFSIL, and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.

Keywords

single-cell RNA-sequencing (scRNA-seq)cellular heterogeneitycell type identificationdata latent shapeclustering

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Tsinghua Science and Technology
Volume 26 Issue 5,
October 2021
Pages 772-789
DOI: 10.26599/TST.2020.9010028
Cite this article:
Tian Y, Zheng R, Liang Z, et al. A Data-Driven Clustering Recommendation Method for Single-Cell RNA-Sequencing Data. Tsinghua Science and Technology, 2021, 26(5): 772-789. https://doi.org/10.26599/TST.2020.9010028

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Received: 03 March 2021
Accepted: 23 March 2021
Published: 20 April 2021
© The author(s) 2021

© The author(s) 2021. The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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