Using microarrays to study the microenvironment in tumor biology: The crucial role of statistics

被引：5

作者：

Baker, Stuart G. ^{[1
]}

Kramer, Barnett S. ^{[2
]}

机构：

[1] NCI, Canc Prevent Div, Bethesda, MD 20892 USA

[2] NIH, Off Dis Prevent, Bethesda, MD 20892 USA

来源：

SEMINARS IN CANCER BIOLOGY | 2008年 / 18卷 / 05期

关键词：

bonferroni; class prediction; cluster analysis; differential expression; false discovery rate; sample size;

D O I：

10.1016/j.semcancer.2008.03.001

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Microarrays represent a potentially powerful tool for better understanding the role of the microenvironment on tumor biology. To make the best use of microarray data and avoid incorrect or unsubstantiated conclusions, care must be taken in the statistical analysis. To illustrate the statistical issues involved we discuss three microarray studies related to the microenvironment and tumor biology involving: (i) prostatic stroma cells in cancer and non-cancer tissues; (ii) breast stroma and epithelial cells in breast cancer patients and non-cancer patients: and (iii) serum associated with wound response and stroma in cancer patients. Using these examples we critically discuss three types of analyses: differential gene expression, cluster analysis, and class prediction. We also discuss design issues. Published by Elsevier Ltd.

引用

页码：305 / 310

页数：6

共 24 条

[1] Microarray data analysis: from disarray to consolidation and consensus [J].