Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations
Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations
复制标题
DOI：
10.1073/pnas.97.18.9834
复制
发表时间：
2000-08-29
影响因子：
11.1
通讯作者：
Sklar, J
中科院分区：
文献类型：
--
作者：
Lee, MLT;Kuo, FC;Sklar, J
我们目前的统计方法分析复制的cDNA微阵列表达数据和报告的对照实验的结果，该研究进行调查基因表达数据的固有变异性和复制在实验中产生更一致和可靠的结果的程度。我们引入了一个统计模型来描述mRNA被包含在目标样本组织中，转化为探针，并最终在载玻片上检测到的概率。我们还介绍了一种方法来分析来自所有重复的组合数据，在这个对照实验中考虑的288个基因中，32个预期会产生强杂交信号，因为已知它们内部存在重复序列。然而，基于单个重复的结果显示，在重复1、2和3中分别有55、36和58个高表达基因。另一方面，通过使用来自所有3个重复的组合数据的分析揭示，288个基因中只有2个被错误地分类为表达的。我们的实验表明，任何单个微阵列输出都受到实质性的变化。通过汇集来自重复的数据，我们可以提供更可靠的基因表达数据分析。因此，我们得出结论，设计重复实验将大大降低误分类率。我们建议在设计实验时至少使用三个重复使用cDNA微阵列，特别是当从单个标本的基因表达数据进行分析。
We present statistical methods for analyzing replicated cDNA microarray expression data and report the results of a controlled experiment, The study was conducted to investigate inherent variability in gene expression data and the extent to which replication in an experiment produces more consistent and reliable findings. We introduce a statistical model to describe the probability that mRNA is contained in the target sample tissue, converted to probe, and ultimately detected on the slide. We also introduce a method to analyze the combined data from all replicates, Of the 288 genes considered in this controlled experiment, 32 would be expected to produce strong hybridization signals because of the known presence of repetitive sequences within them. Results based on individual replicates, however, show that there are 55, 36, and 58 highly expressed genes in replicates 1, 2, and 3, respectively. On the other hand, an analysis by using the combined data from all 3 replicates reveals that only 2 of the 288 genes are incorrectly classified as expressed, Our experiment shows that any single microarray output is subject to substantial variability. By pooling data from replicates, we can provide a more reliable analysis of gene expression data. Therefore, we conclude that designing experiments with replications will greatly reduce misclassification rates. We recommend that at least three replicates be used in designing experiments by using cDNA microarrays, particularly when gene expression data from single specimens are being analyzed.