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Open Access

G3DC: A Gene-Graph-Guided Selective Deep Clustering Method for Single Cell RNA-seq Data

Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen 518172, China, and also with Shenzhen Research Institute of Big Data, Shenzhen 518172, China
School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen 518172, China, and also with Warshel Institute for Computational Biology, Shenzhen 518172, China
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Abstract

Single-cell RNA sequencing (scRNA-seq) technology measures the expression of thousands of genes at the cellular level. Analyzing single-cell transcriptome allows the identification of heterogeneous cell groups, cellular-level regulations, and the trajectory of cell development. An important aspect in the analyses of scRNA-seq data is the clustering of cells, which is hampered by issues, such as high dimensionality, cell type imbalance, redundancy, and dropout. Given cells of each type are functionally consistent, incorporating biological relations among genes may improve the clustering results. In light of this, we have developed a deep-embedded clustering method, G3DC. This method combines a graph regularization based on the pre-existing gene network and a feature selector based on the ℓ2,1-norm regularization, along with a reconstruction loss, to generate a discriminatory and informative embedding. Utilizing the gene interaction network bolsters the clustering performance and aids in selecting functionally coherent genes, consequently enriching the clustering results. Extensive experiments have shown that G3DC offers high clustering accuracy with regard to agreement with true cell types, outperforming other leading single-cell clustering methods. In addition, G3DC selects biologically relevant genes that contribute to the clustering, providing insight into biological functionality that differentiates cell groups.

Keywords

deep learningfeature selectiongene graphs

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 809-827
DOI: 10.26599/BDMA.2024.9020011
Cite this article:
He S, Fan J, Yu T. G3DC: A Gene-Graph-Guided Selective Deep Clustering Method for Single Cell RNA-seq Data. Big Data Mining and Analytics, 2024, 7(3): 809-827. https://doi.org/10.26599/BDMA.2024.9020011

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Received: 09 October 2023
Revised: 22 February 2024
Accepted: 23 February 2024
Published: 28 August 2024
© The author(s) 2024.

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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