Pathways to the analysis of microarray data

被引:219
作者
Curtis, RK
Oresic, M
Vidal-Puig, A
机构
[1] Univ Cambridge, Dept Clin Biochem, Addenbrookes Hosp, Cambridge CB2 2QR, England
[2] Tech Res Ctr Finland, VTT Biotechnol, FIN-02044 Espoo, Finland
基金
英国惠康基金; 英国医学研究理事会;
关键词
D O I
10.1016/j.tibtech.2005.05.011
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.
引用
收藏
页码:429 / 435
页数:7
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