Ding Qian, Sun Yan, Shang Junliang, Li Feng, Zhang Yuanyuan, Liu Jin-Xing
School of Computer Science, Qufu Normal University, Rizhao, China.
School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China.
Front Genet. 2021 Jul 22;12:678642. doi: 10.3389/fgene.2021.678642. eCollection 2021.
Pancreatic cancer (PC) is a highly fatal disease, yet its causes remain unclear. Comprehensive analysis of different types of PC genetic data plays a crucial role in understanding its pathogenic mechanisms. Currently, non-negative matrix factorization (NMF)-based methods are widely used for genetic data analysis. Nevertheless, it is a challenge for them to integrate and decompose different types of genetic data simultaneously. In this paper, a non-NMF network analysis method, NMFNA, is proposed, which introduces a graph-regularized constraint to the NMF, for identifying modules and characteristic genes from two-type PC data of methylation (ME) and copy number variation (CNV). Firstly, three PC networks, i.e., ME network, CNV network, and ME-CNV network, are constructed using the Pearson correlation coefficient (PCC). Then, modules are detected from these three PC networks effectively due to the introduced graph-regularized constraint, which is the highlight of the NMFNA. Finally, both gene ontology (GO) and pathway enrichment analyses are performed, and characteristic genes are detected by the multimeasure score, to deeply understand biological functions of PC core modules. Experimental results demonstrated that the NMFNA facilitates the integration and decomposition of two types of PC data simultaneously and can further serve as an alternative method for detecting modules and characteristic genes from multiple genetic data of complex diseases.
胰腺癌（PC）是一种致命性很高的疾病，但其病因仍不清楚。对不同类型的PC基因数据进行综合分析对于理解其致病机制起着至关重要的作用。目前，基于非负矩阵分解（NMF）的方法被广泛用于基因数据分析。然而，让它们同时整合和分解不同类型的基因数据是一项挑战。本文提出了一种非NMF网络分析方法NMFNA，该方法在NMF中引入了图正则化约束，用于从甲基化（ME）和拷贝数变异（CNV）这两种类型的PC数据中识别模块和特征基因。首先，使用皮尔逊相关系数（PCC）构建三个PC网络，即ME网络、CNV网络和ME-CNV网络。然后，由于引入了图正则化约束，从这三个PC网络中有效地检测出模块，这是NMFNA的亮点。最后，进行基因本体（GO）和通路富集分析，并通过多指标评分检测特征基因，以深入了解PC核心模块的生物学功能。实验结果表明，NMFNA有助于同时整合和分解两种类型的PC数据，并可进一步作为从复杂疾病的多种基因数据中检测模块和特征基因的替代方法。
