使用非负矩阵因子分解对“组学数据”进行综合亚型分析的随机奇异值分解。

IF 0.9 4区 数学 Q3 Mathematics
Yonghui Ni, Jianghua He, Prabhakar Chalise
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引用次数: 0

摘要

整合多组学数据集以区分癌症亚型是一种强大的技术,可以利用多组学的一致性和互补性信息。矩阵分解是综合聚类中用于识别多组学数据中潜在亚型结构的常用技术。组学数据的高维性和长计算时间一直是聚类方法的常见挑战。为了应对这些挑战,我们提出了使用非负矩阵因子分解进行综合聚类的随机奇异值分解(RSVD):intNMF-RSVD。该方法利用RSVD将数据投影到具有用户指定的较低秩的特征向量空间中来降维。然后,通过估计投影的多组学数据集的公共基矩阵来进行聚类分析。使用模拟数据集评估了所提出方法的性能,并使用癌症基因组图谱研究的真实数据集与六种最先进的综合聚类方法进行了比较。发现与标准intNMF和其他多组分聚类方法相比,intNMF-rsvd有效且具有竞争力。最重要的是,intNMF-rsvd可以处理大量特征,并显著减少计算时间。确定的亚型可用于进一步的临床关联研究,以了解疾病的病因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization.

Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.

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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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