Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data.

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yaxin Xu, Wei Zhang, Xiaoying Zheng, Xianxian Cai
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引用次数: 0

Abstract

Single-cell RNA sequencing technology is one of the most cost-effective ways to uncover transcriptomic heterogeneity. With the rapid rise of this technology, enormous amounts of scRNA-seq data have been produced. Due to the high dimensionality, noise, sparsity and missing features of the available scRNA-seq data, accurately clustering the scRNA-seq data for downstream analysis is a significant challenge. Many computational methods have been designed to address this issue; nevertheless, the efficacy of the available methods is still inadequate. In addition, most similarity-based methods require a number of clusters as input, which is difficult to achieve in real applications. In this study, we developed a novel computational method for clustering scRNA-seq data by considering both global and local information, named GCFG. This method characterizes the global properties of data by applying concept factorization, and the regularized Gaussian graphical model is utilized to evaluate the local embedding relationship of data. To learn the cell-cell similarity matrix, we integrated the two components, and an iterative optimization algorithm was developed. The categorization of single cells is obtained by applying Louvain, a modularity-based community discovery algorithm, to the similarity matrix. The behavior of the GCFG approach is assessed on 14 real scRNA-seq datasets in terms of ACC and ARI, and comparison results with 17 other competitive methods suggest that GCFG is effective and robust.

Abstract Image

结合全局约束概念因子分解和正则化高斯图形模型对单细胞RNA-seq数据进行聚类。
单细胞RNA测序技术是揭示转录组异质性的最具成本效益的方法之一。随着这项技术的迅速兴起,已经产生了大量的scRNA-seq数据。由于可用的scRNA-seq数据的高维度、噪声、稀疏性和缺失特征,准确地对scRNA-seq数据进行聚类以进行下游分析是一个重大挑战。已经设计了许多计算方法来解决这个问题;然而,现有方法的有效性仍然不足。此外,大多数基于相似性的方法都需要大量的聚类作为输入,这在实际应用中很难实现。在这项研究中,我们开发了一种新的计算方法,通过考虑全局和局部信息对scRNA-seq数据进行聚类,称为GCFG。该方法利用概念分解来刻画数据的全局性质,并利用正则化高斯图形模型来评估数据的局部嵌入关系。为了学习细胞-细胞相似性矩阵,我们集成了这两个组件,并开发了一个迭代优化算法。单细胞的分类是通过将基于模块化的社区发现算法Louvain应用于相似性矩阵来获得的。根据ACC和ARI,在14个真实的scRNA-seq数据集上评估了GCFG方法的行为,与其他17种竞争方法的比较结果表明,GCFG是有效和稳健的。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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