Analysis of variance for gene expression microarray data
Analysis of variance for gene expression microarray data
复制标题
DOI：
10.1089/10665270050514954
复制
发表时间：
2000-01-01
影响因子：
1.7
通讯作者：
Churchill, GA
中科院分区：
文献类型：
--
作者：
Kerr, MK;Martin, M;Churchill, GA
斑点cDNA微阵列作为一种强大且经济的大规模基因表达分析工具正在兴起，微阵列可用于同时测量两个或多个组织样本中数千个基因的特定mrna的相对数量。虽然这项技术的力量已经得到认可，但关于微阵列数据的适当分析仍然存在许多悬而未决的问题。一个问题是如何对不受辅助变异来源影响的基因的相对表达进行有效估计。认识到微阵列数据中存在固有的“噪声”，如何估计与估计的表达变化相关的误差变化，即如何构建误差条？我们证明，方差分析方法可用于标准化微阵列数据，并提供基因表达变化的估计，以纠正潜在的混杂效应。这种方法为微阵列数据的一般分析和解释建立了一个框架。
Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large-scale analysis of gene expression, Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise'' in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data.