A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy.
A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy.
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DOI：
10.1186/1471-2164-9-98
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发表时间：
2008-02-26
期刊：
影响因子：
4.4
通讯作者：
Archer KJ
中科院分区：
文献类型：
--
作者：
Kong X;Mas V;Archer KJ
随着DNA微阵列技术的普及，多组研究人员对相似生物条件下的基因表达进行了研究。已经开发了不同的方法来整合来自各种微阵列研究的结果，尽管它们中的大多数依赖于分布假设，例如基于t统计的混合效应模型或贝叶斯模型方法。然而，每个单独的微阵列实验的样本量通常很小。因此，在本文中，我们提出了一种非参数荟萃分析方法，用于结合独立微阵列研究的数据，并说明了其在两项独立Affymetrix基因芯片研究中的应用，这些研究比较了患有慢性同种移植肾病（CAN）的肾移植受者活检组织的基因表达。与功能正常的同种移植物。模拟研究比较了非参数荟萃分析方法和常用的基于t统计量的方法，结果表明非参数方法具有更好的敏感性和特异性。对于两项CAN研究的应用，我们确定了309个在CAN中表达不同的不同基因。通过应用Fisher精确检验来鉴定在那些被称为差异表达的基因中富集的KEGG通路，我们发现6个KEGG通路在所鉴定的基因中过度表达。我们使用所识别基因的表达测量作为预测因子来预测另外6个活检样本的类别标签，并且预测结果均符合其病理学家诊断的类别标签。我们提出了一种新的方法，结合多个独立的微阵列研究的数据。该方法为非参数方法，不依赖任何分布假设。这种方法背后的原理在逻辑上是直观的，没有受过统计学高级培训的研究人员很容易理解。据报道，一些已鉴定的基因和途径与肾脏疾病相关。对已鉴定的基因和通路的进一步研究可能会在分子水平上更好地理解CAN。
With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN) to those with normal functioning allograft. The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been reported to be relevant to renal diseases. Further study on the identified genes and pathways may lead to better understanding of CAN at the molecular level.
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影响因子：
3
作者：
Stevens, JR;Doerge, RW
通讯作者：
Doerge, RW
影响因子：
3
作者：
Hu P;Greenwood CM;Beyene J
通讯作者：
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影响因子：
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作者：
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DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
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通讯作者：
Chu, G
影响因子：
46.9
作者：
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通讯作者：
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