Title: A network enhancement-based method for clustering of single cell RNA-seq data

Authors: Xiaoshu Zhu; Lilu Guo; Rongyuan Li; Yunpei Xu; Fang-Xiang Wu; Xiaoqing Peng; Hong-Dong Li

Addresses: Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China; School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi, China ' School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi, China ' School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi, China ' Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China ' Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada ' Centre for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China ' Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China

Abstract: Single cell RNA sequencing (scRNA-seq) provides a more granular description of gene expression in a single cell. Many clustering methods for scRNA-seq data have been developed to understand cell development and cell differentiation. However, the high dimension and the strong noise make clustering scRNA-seq data challenging. To overcome this problem, we propose a method for clustering scRNA-seq data, called network enhancement-based similarity combined with Louvain (NES-Louvain). In NES-Louvain, the initial similarity matrix is denoised by using a network enhancement method. Then, a path-based similarity measurement is designed to introduce the nodes in high-order paths based on the assumption that including more relevant nodes would improve the similarity of node pairs. Finally, the Louvain community detection method is improved to clustering single cells. The experimental results show that NES and NES-Louvain achieve better performance than other methods. Furthermore, NES-Louvain shows robust to perturbation.

Keywords: similarity measurement; single cell clustering; network enhancement; path-based similarity; Louvain community detection.

DOI: 10.1504/IJDMB.2020.113690

International Journal of Data Mining and Bioinformatics, 2020 Vol.24 No.4, pp.306 - 325

Received: 21 Jul 2020
Accepted: 20 Sep 2020
Published online: 18 Mar 2021 *

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