Gene Expression Data Analysis of Different Brain Areas Based on Non-Negative Matrix Factorization
1 Information Engineering College, Shanghai Maritime University, Shanghai 201306, China
2 Department of Chemistry and Biochemistry, Rowan University, NJ 08028, USA
摘要基因之间存在多种多样的表达调控活动，一般认为这些调控关系隐含在基因表达谱中。针对阿尔茨海默症(AD) 起病隐匿、诊断难、发病机理复杂以及基因信号传导通路和调控关系难以重建等特征，利用非平滑非负矩阵分解(nsNMF)方法提取AD致病基因，聚类过程中利用共表型相关性系数(CCC)选取聚类数 k 的值，得到最优的聚类数目。针对基因表达数据噪声高、信息变量隐藏难分析的困难，考虑AD的发生发展与许多大脑功能区域密切相关的特性，提出将nsNMF分别应用于AD患者的大脑海马区、内嗅区皮质、颞中回及视觉皮层区的基因表达数据中，共提取3 800个显著基因，其中包括确定与AD致病机理有关联的10个致病基因，并进行了生物学分析，得到了AD相关的细胞凋亡、代谢及炎症反应等生物过程，显示nsNMF方法及大脑多区域数据集的联合分析能更全面地探寻AD信号传导关系及基因调控方式。
Abstract：It is accounted that various regulatory activities between genes contain in the gene expression datasets. Alzheimers disease (AD) are characterized by its hidden onset, complex pathological mechanism, hard diagnosis and it is difficult to reconstruct the genes signal pathways and its regulatory network. In this work, we improved nonsmooth nonnegative matrix factorization (nsNMF) to identify significant genes of Alzheimers disease (AD) using cophenetic correlation coefficient to confirm the clustering number k. Since gene expression dataset has high noise, and the underlying information is hard to analyze according to the function of brain areas, we applied nsNMF to AD samples of hippocampus (HIP), entorhinal cortex (EC), media temporal gyrus (MTG) and primary visual cortex (VCX) which have close relationship of human learning and memory. After that, 3800 of significant genes were extracted including 10 known pathogenic genes. By biological analysis, many AD related biological process like apoptosis, metabolize and inflammation were obtained, and it is demonstrated that the improved nsNMF and the conjoint analysis method can deeply explore the pathways and gene regulatory ways of AD.
孔薇1* 陶伟杰1 牟晓阳2. 基于非负矩阵分解的大脑不同区域基因表达数据分析[J]. 中国生物医学工程学报, 2012, 31(6): 875-881.
KONG Wei 1* TAO Wei Jie 1 MOU Xiao Yang2. Gene Expression Data Analysis of Different Brain Areas Based on Non-Negative Matrix Factorization. journal1, 2012, 31(6): 875-881.
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