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首页> 外文期刊>BMC Genomics >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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A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy

机译：一种用于组合独立微阵列数据集的非参数荟萃分析方法：使用两个与慢性同种异体移植肾病有关的微阵列数据集的应用

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Background 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. Results 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. Conclusion 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.
机译：背景技术随着DNA芯片技术的普及，多组研究人员研究了相似生物学条件的基因表达。尽管大多数方法都依赖于分布假设，例如基于t统计量的混合效应模型或贝叶斯模型方法，但已开发出各种方法来整合各种微阵列研究的结果。但是，每个单个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，用于合并来自独立微阵列研究的数据，并说明了其在两项独立的Affymetrix GeneChip研究中的应用，该研究比较了来自肾脏移植接受者与慢性同种异体肾病（CAN）的活检组织）给同种异体功能正常的人。结果将非参数荟萃分析方法与常用的基于t统计量的方法进行比较的仿真研究表明，非参数方法具有更好的敏感性和特异性。对于两次CAN研究的应用，我们鉴定了309个在CAN中表达不同的不同基因。通过采用Fisher精确检验在那些差异表达的基因中鉴定出丰富的KEGG途径，我们发现了6条KEGG途径在鉴定的基因中被过度表达。我们使用鉴定出的基因的表达量作为预测因子来预测另外6个活检样本的分类标签，并且预测结果均符合其病理学家诊断的分类标签。结论我们提出了一种结合来自多个独立微阵列研究的数据的新方法。这种方法是非参数的，并且不依赖于任何分布假设。该方法的基本原理在逻辑上是直观的，并且未经统计方面的高级培训的研究人员可以轻松理解。据报道，某些已鉴定的基因和途径与肾脏疾病有关。对鉴定出的基因和途径的进一步研究可能会导致在分子水平上对CAN的更好理解。

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