复制
统计的
背景(考古学)
DNA微阵列
统计
贝叶斯定理
微阵列分析技术
数据集
微阵列
基因表达谱
计算生物学
计算机科学
数学
生物
基因表达
基因
贝叶斯概率
遗传学
古生物学
摘要
cDNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many different contexts to compare mRNA levels between two or more samples of cells. Microarray experiments typically give us expression measurements on a large number of genes, say 10,000-20,000, but with few, if any, replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not completely satisfactory in this context, and so a different approach seems desirable. In this paper we present an empirical Bayes method for analysing replicated microarray data. Data from all the genes in a replicate set of experiments are combined into estimates of parameters of a prior distribution. These parameter estimates are then combined at the gene level with means and standard deviations to form a statistic B which can be used to decide whether differential expression has occurred. The statistic B avoids the problems of using averages or t-statistics. The method is illustrated using data from an experiment comparing the expression of genes in the livers of SR-BI transgenic mice with that of the corresponding wild-type mice. In addition we present the results of a simulation study estimating the ROC curve of B and three other statistics for determining differential expression: the average and two simple modifications of the usual t-statistic. B was found to be the most powerful of the four, though the margin was not great. The data were simulated to resemble the SR-BI data.
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