【作者】 王魏强；

【导师】 周艳红；

【作者基本信息】 华中科技大学 ， 计算机应用技术， 2006， 硕士

【摘要】 DNA微阵列已经在功能基因组学研究中获得了广泛的应用。在人类疾病研究领域,分离出在疾病状态下特异的基因表达变化具有十分重要的研究意义和实用价值。如何从众多差异表达基因中分离出那些跟疾病形成与发展相关的表达变化,已经成为疾病基因表达谱数据分析的一个关键问题,目前的差异分析方法还不能完全解决这个问题。为了解决这个问题,提出了系统的基因表达谱差异分析方法。通过整合基因水平差异分析和功能水平差异分析,从基因、功能两个层次上研究基因表达变化与疾病表型的关联。基因水平差异分析采用了修正的T检验算法来分析单个基因的表达变化,并用显著性p值来描述变化程度;功能水平差异分析采用了组合的S检验,通过整合基因的功能编目分类信息,利用单个基因差异表达的定量信息,研究一组功能相关基因的一致性表达变化。基于上述的分析策略,开发了基因表达谱差异分析软件MageKey(v1.0)。利用MageKey分析一组人类肌萎缩性脊髓侧索硬化症疾病的基因表达谱数据,测试结果显示了这种分析策略能够有效地建立疾病表型与基因和功能之间的关联。通过整合功能注释信息,从基因水平和功能水平研究基因表达变化与疾病表型的关联,这种分析策略能够快速地将疾病表型与功能失调及其表达变化关联起来,帮助生成一些关于疾病机制的生物学假设。同时本文的结果也说明了整合功能注释信息研究基因表达谱数据的重要性。更多还原

【Abstract】 Genome-wide differential expression studies of human diseases using microarray technology usually produce long lists of genes with altered expression, therefore, the genes causally involved in a disease cannot be effectively separated from innocent bystanders. Existing methods for differential analysis of gene expression profiles seem unable to solve this problem successfully.We present a systematic strategy that combines gene-wise and function-wise differential analysis of gene expression profiles to interrelate genes and functions with human diseases. The gene-wise analysis adopts a modified T-test to analyze the expression alteration of each single gene, and the alteration is represented by quantitative significant p-value. The function-wise analysis uses a new combined S-test to identify coordinate alterations of genes within each functional category.A computational tool, MageKey, is developed based on this strategy, and its utility is demonstrated by the analysis results of gene expression dataset of human Amyotrophic Lateral Sclerosis (ALS) disease. MageKey is able to associate relevant biological functions with human disease.The association between disease phenotype and gene expression change is investigated by integrating functional annotation information. Such analysis strategy is able to effectively interrelate genes and functions with human disease, and help generate biology hypothesis. Meanwhile, our results associating genes and functions with human disease illustrate the value of integrating functional annotation information in the analysis of gene expression profiles.更多还原