期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

显著性分析(SAM)方法在乳腺癌基因芯片数据分析中的应用 被引量：2

The Application of Significance Analysis of Microarrays in Data Analysis of Breast Cancer Microarray
原文传递
导出
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.
作者 罗亚玲 蒋峥 张世强
机构地区 重庆医科大学数学教研室
出处 《数学的实践与认识》 北大核心 2015年第1期112-118,共7页 Mathematics in Practice and Theory
关键词 基因芯片 乳腺癌 permutation检验 显著性分析 SAM microarray breast cancer permutation test significance analysis of microar- rays SAM
分类号 R737.9 [医药卫生—肿瘤]
  • 相关文献

参考文献10

  • 1Ross DT, Scherf U, Eisen MB, et al. Systematic variation in gene expression patterns in human cancer cell lines[J]. Nat Genet, 2000, 24(3): 227-235.
  • 2杨晶,王兆月,田心.乳腺癌基因芯片数据使用探讨[J].生物信息学,2007,5(1):23-24. 被引量：2
  • 3荀鹏程,赵杨,易洪刚,柏建岭,于浩,陈峰.Permutation Test在假设检验中的应用[J].数理统计与管理,2006,25(5):616-621. 被引量：38
  • 4Tusher V G, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response[J]. Proc Natl Acad Sci USA, 2001, 98(9): 5116-5121.
  • 5Vecchi M, Confalonieri S, Nuciforo P, Vigan5 MA, Capra M, Bianchi M, Nicosia D, Bianchi F, Galimberti V, Viale G, Palermo G, Riccardi A, Campanini R, Daidone M G, Pierotti M A, Pece S, Di Fiore P P. Breast cancer metastases are molecularly distinct from their primary tumors[J]. Oncogene, 2008, 27(15): 2148-58.
  • 6Teoh S S, Whisstock J C, Bird P I. Maspin (SERPINB5) is an obligate intracellular serpin[J]. J Biol Chem. 2010, 285(14):10862-9.
  • 7Pluciennik E, Krol M, Nowakowska M, Kus]nska R, Potemski P, Kordek R, Bednarek A K. Breast cancer relapse prediction based on multi-gene RT-PCR algorithm[J]. Med Sci Monit. 2010, 16(3): CR132-136.
  • 8Seitz S, Korsching E, Weimer J, Jacobsen A, Arnold N, Meindl A, Arnold W, Gustavus D, Klebig C, Petersen I, Scherneck S. Genetic background of different cancer cell lines influences the gene set involved in chromosome 8 mediated breast tumor suppression[J]. Genes Chromosomes Cancer. 2006, 45(6): 612-27.
  • 9Satoh K, Hata M, Yokota H. High lib mRNA expression in breast carcinomas[J]. DNA Res. 2004, 11(3): 199-203.
  • 10Horvath S, Zhang B, Carlson M, Lu K V, Zhu S, Felciano R M, Laurance M F, Zhao W, Qi S, Chen Z, Lee Y, Scheck A C, Liau L M, Wu H, Geschwind D H, Febbo P G, Kornblum H I, CloughesyT F, Nelson S F, Mischel P S. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target[J]. Proc Natl Acad Sci USA. 2006, 103(46): 17402-7.

二级参考文献14

  • 1[1]Michael Y.Galperin.The Molecular Biology Database Collection:2005 update[J].Nucleic Acids Research,2005,33(Database Issue):D5-D24.
  • 2[2]Michael Y.Galperin.The Molecular Biology Database collection:2006 update[J].Nucleic Acids Research,2006,34(Database Issue):D3-D5.
  • 3[3]Brazma A.,Hingamp P.,Quackenbush J.,et al.Minimum information about a microarray experiment (MIAME) toward standards for microarray data[J].Nature Genetics,2001 Dec,29(4):365-71.
  • 4[4]Spellman P.T.,Miller M.,Stewart J.,et al.Design and implementation of microarray gene expression markup language(MAGE-ML)[J].Genome Biology,2002,3(9):reasearch 0046.1-0046.9.
  • 5[6]Edgar R,Domrachev M,Lash AE.Gene Expression Omnibus:NCBI gene expression and hybridization array data repository[j].Nucleic Acids Research.2002,30(1):207-210.
  • 6H. Onder, M. Sahin, S. Cankaya, Y. Yahtah,Z. Cebeci. Use of t-statistics with permutation test: for small sample size. Procedings-International Congress on information technology in agriculture, food and environment.http:cebeciz.cu.edu.tr/documents/pdf/Use Of Permutation Tests-Itafe 03.pdf(2003)
  • 7Sayan Mukherjee, Polina Golland and Dmitry Panehenko(2003). Permutation Tests for Classification.http://cbcl.mit.edu/cbcl/publications/ai-publications/2003/AIM-2003-019.pdf(2003)
  • 8Wei Pan. On the use of permutation in and the performance of a class of nonparametrie methods to detect differential gene expression, [ J]. Bioinformatics, 2003,19:1333 - 1340.
  • 9SEUNG-Ho KANG,HYUNG W. KIM and CHUL W. AHN.A permutation test for nondependent matched pair data,[J]. Drug Information Journal, 2001,35:407 - 411.
  • 10E. S. Venkatraman. Apermutation test to compare receiver operating characteristic curves, [ J]. Biometrics,2000,56:1134 - 1138.

共引文献38

同被引文献6

引证文献2

二级引证文献4

数学的实践与认识
2015年 第1期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部