显著性分析(SAM)方法在乳腺癌基因芯片数据分析中的应用  

The Application of Significance Analysis of Microarrays in Data Analysis of Breast Cancer Microarray

作　　者：罗亚玲[1] 蒋峥[1] 张世强[1] 

机构地区：[1]重庆医科大学数学教研室,重庆400016

出　　处：《数学的实践与认识》2015年第1期112-118,共7页Mathematics in Practice and Theory

摘　　要：对乳腺癌基因芯片试验结果进行数据分析,寻找在正常组织与癌组织中呈现差异表达的基因.运用微阵列芯片显著性分析(SAM)方法进行差异表达基因的筛选,并使用permutation算法计算错误发现率(FDR).一些呈现差异表达的基因被筛选出来,其中一部分基因已被数篇文献报道过,认为它与乳腺癌发病相关.SAM方法比较适用于对基因芯片实验的结果进行相关基因的初步筛选,筛选出的基因可用于为进一步的研究提供候选基因.Object： Analysis the data of breast cancer microarrays downloaded from public gene databases to find out differential expressed genes between normal tissues and cancer tissues. Method： Choose the significance analysis of microarrays （SAM） method to find out differential expressed genes, and compute the false discovery rate （FDR） using permutation method. Results： A number of differential expressed genes were selected, part of which were reported to be associated with breast cancer by several papers. Conclusion： SAM is suit for choosing differential expressed genes from microarrays preliminary, and the genes selected can be used to provide candidate genes for further study.

关 键 词：基因芯片 乳腺癌 permutation检验 显著性分析 SAM 

分 类 号：R737.9[医药卫生—肿瘤]