基于大脑不同区域的阿尔茨海默症基因表达数据分析

Gene Expression Data Analysis of Alzheimer's Disease Based on Different Brain Areas

提出了采用Tukey双权函数作为FastICA(Fast Independent Component Analysis)方法的非线性函数,对阿尔茨海默症(Alzheimer’s disease,AD)多个脑区域基因表达数据进行显著基因提取,揭示其基因表达调控关系.针对传统聚类方法基于全局聚类且只能将某个基因聚类到某一类的缺陷,改进的FastICA方法能够对基因表达数据进行快速有效的双向聚类,能够满足同一个基因可能参与不同信号传导通路的生物特性.同时考虑到人脑中海马区、内嗅皮质区、颞中回及视觉皮层区均与学习与记忆功能密切相关,将算法对多个脑区域进行基因表达调控综合分析.结果表明,大量炎症反应是AD致病的重要因素之一.

An improved FastICA （Fast Independent Component Analysis） algorithm using Tukey biweight function as its nonlinear function was proposed to analyze significant genes and regulatory network of multi-brain areas of Alzheimer＇s disease （AD）. To avoid the limitation of traditional clustering methods which group genes in only one class and based on the global similarities in their expression profiles, in this study, the improved biclustering method can identify the significant genes and gene regulatory modules of AD efficiently. According to the function of brain area, this method was applied to the AD brain samples of hippocampus （HIP）, entorhinal cortex （EC）, media temporal gyrus （MTG） and primary visual cortex respectively which was closely related to human learning and memory. The integrated biological analysis demonstrated that the identified inflammation processes in human brain played an important role in AD.