论文部分内容阅读
目的 探讨从巨量的微阵列基因数据中挖掘肿瘤相关分子机理及功能信息 ;方法 以人肿瘤cDNA微阵列1 2分析并获得间变性星形细胞瘤和正常脑组织的差异表达基因数据 ,进行基于CityBlock距离和平均距离法的聚类分析 ,应用超几何分布的概率模型计算聚类分析所得的各基因类与GO数据库注释的各基因功能类之间的随机关联概率 ,给各基因类标以显著关联的GO数据库的功能类标签。结果 从数以千计的基因数据中获得了 12 1个差异显示基因并分成 6类 ,这 6类基因的生物学功能基本与该肿瘤的生物学特点符合。GO数据库中 6个基因通路功能类分别与该分类相关度最大。结论 应用聚类分析方法和GO数据库对微阵列研究获得的基因信息进行进一步分析 ,有利于提取巨量基因数据中的有效信息 ,可能提供进一步研究的有价值线索。
OBJECTIVE: To explore the mechanism and functional information of tumor-related genes from the huge amount of microarray gene data. Methods The differentially expressed astrocytomas and normal brain tissues were analyzed by cDNA microarray 12, CityBlock distance and average distance method of clustering analysis, the use of hypergeometric distribution of the probability model to calculate the cluster analysis of each gene and GO database annotation of the gene function of the random association between the probabilities of each gene marked marked Associated GO database function class label. Results Twelve differential display genes were obtained from thousands of genetic data and divided into six categories. The biological functions of these six genes were basically consistent with the biological characteristics of the tumor. The 6 gene function categories in GO database had the highest correlation with this classification respectively. Conclusion Further analysis of gene information obtained from microarray using cluster analysis and GO database is helpful to extract valid information from huge gene data and may provide valuable clues for further research.