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Hierarchical Bayesian meta-analysis models for cross-platform microarray studies

机译：跨平台微阵列研究的分层贝叶斯荟萃分析模型

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摘要

The development of new technologies to measure gene expression has been calling for statistical methods to integrate findings across multiple-platform studies. A common goal of microarray analysis is to identify genes with differential expression between two conditions, such as treatment versus control. Here, we introduce a hierarchical Bayesian meta-analysis model to pool gene expression studies from different microarray platforms: spotted DNA arrays and short oligonucleotide arrays. The studies have different array design layouts, each with multiple sources of data replication, including repeated experiments, slides and probes. Our model produces the gene-specific posterior probability of differential expression, which is the basis for inference. In simulations combining two and five independent studies, our meta-analysis model outperformed separate analyses for three commonly used comparison measures; it also showed improved receiver operating characteristic curves. When combining spotted DNA and CombiMatrix short oligonucleotide array studies of Geobacter sulfurreducens, our meta-analysis model discovered more genes for fixed thresholds of posterior probability of differential expression and Bayesian false discovery than individual study analyses. We also examine an alternative model and compare models using the deviance information criterion.
机译：测量基因表达的新技术的发展一直要求采用统计方法来整合跨多平台研究的结果。微阵列分析的共同目标是鉴定在两种条件（例如治疗与对照）之间差异表达的基因。在这里，我们介绍了一个分级贝叶斯元分析模型，以汇集来自不同微阵列平台的基因表达研究：斑点DNA阵列和短寡核苷酸阵列。这些研究具有不同的阵列设计布局，每种布局都有多种数据复制来源，包括重复的实验，载玻片和探针。我们的模型产生差异表达的基因特异性后验概率，这是推断的基础。在结合两个和五个独立研究的模拟中，我们的荟萃分析模型在三种常用的比较方法上胜过了单独的分析；它还显示了改善的接收器工作特性曲线。当结合斑点DNA和CombiMatrix还原硫短杆菌的短寡核苷酸阵列研究时，我们的荟萃分析模型发现差异表达和贝叶斯错误发现的后验概率固定阈值的基因比单独的研究分析更多。我们还将检查替代模型并使用偏差信息标准比较模型。

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