用于样本聚类和网络分析的整合鲁棒结构化NMF模型

doi:10.3969/j.issn.0255-8297.2020.05.012

摘要/Abstract

摘要： 为了更好地保留数据之间的同质性，提出了一种整合鲁棒结构化非负矩阵分解（integrated robust structured non-negative matrix factorization，iRSNMF）模型，并在该模型中引入一个结构化项.将该模型用于癌症样本聚类实验和基因共表达网络分析，以验证其有效性.根据现有文献对相关基因和通路进行生物学解释.实验结果表明，iRSNMF模型聚类性能较好并且能够挖掘到的关键基因更多.用iRSNMF模型获得的基因和通路在癌症的发病机制中起着重要作用，并为癌症诊断、治疗和预后提供了新的思路.

关键词: 整合模型, 结构化, 非负矩阵分解, 样本聚类, 基因共表达网络分析

Abstract: In order to preserve the homogeneity among data more effectively, this paper proposes an integrated robust structured non-negative matrix factorization (integrated robust structured non-negative matrix factorization, iRSNMF) model with an induced structured term. We verify the effectiveness of this model by applying it to the clustering experiments of cancer samples and the analysis of gene co-expression network. Reasonable biological explanations of related genes and pathways are given based on existing literature. Experimental results show that the iRSNMF method has excellent clustering performance and more-key genes mining ability. The genes and pathways obtained by the iRSNMF model play an important role in cancer pathogenesis, accordingly, providing a new idea for the diagnosis, treatment and prognosis of cancer.

Key words: integrated model, structured, non-negative matrix factorization, sample clustering, gene co-expression network analysis

中图分类号:

TP399

张晓宁, 孔祥真, 罗传文, 刘金星. 用于样本聚类和网络分析的整合鲁棒结构化NMF模型[J]. 应用科学学报, 2020, 38(5): 825-842.

ZHANG Xiaoning, KONG Xiangzhen, LUO Chuanwen, LIU Jinxing. Integrated Robust Structured NMF Model for Sample Clustering and Network Analysis[J]. Journal of Applied Sciences, 2020, 38(5): 825-842.

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