Combining Affymetrix microarray results
Combining Affymetrix microarray results
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DOI：
10.1186/1471-2105-6-57
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发表时间：
2005-03-17
影响因子：
3
通讯作者：
Doerge, RW
中科院分区：
文献类型：
--
作者：
Stevens, JR;Doerge, RW
背景：随着微阵列技术的使用变得越来越普遍，几个实验室使用相同的微阵列技术在同一物种中鉴定与相同疾病相关的基因并不少见。结果：我们提出了一种基于统计学的荟萃分析方法用于微阵列分析，目的是系统地结合不同实验室的结果。这种方法提供了对与感兴趣的条件显著相关的基因的更准确的看法，同时允许不同实验室之间的差异。特别令人感兴趣的是广泛使用的Affymetrix寡核苷酸阵列，其结果自然适合荟萃分析。基于Affymetrix平台开发了一个模拟模型，以检验元分析方法的适应性，并说明这种方法在合并跨实验室的微阵列结果方面的有用性。然后将该方法应用于涉及多个硬化症的小鼠模型的真实数据。结论：Meta分析模型的定量估计往往比任何单一实验室更接近差异表达的“真实”程度。荟萃分析方法可以系统地结合来自不同实验室的Affymetrix结果，以更清楚地了解基因与特定感兴趣条件的关系。
Background: As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis.Results: We propose a statistically-based meta-analytic approach to microarray analysis for the purpose of systematically combining results from the different laboratories. This approach provides a more precise view of genes that are significantly related to the condition of interest while simultaneously allowing for differences between laboratories. Of particular interest is the widely used Affymetrix oligonucleotide array, the results of which are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the usefulness of such an approach in combining microarray results across laboratories. The approach is then applied to real data involving a mouse model for multiple sclerosis.Conclusion: The quantitative estimates from the meta-analysis model tend to be closer to the "true" degree of differential expression than any single lab. Meta-analytic methods can systematically combine Affymetrix results from different laboratories to gain a clearer understanding of genes' relationships to specific conditions of interest.