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Large-scale simultaneous inference with applications to the detection of differential expression with microarray data

机译：大规模同时推断及其在微阵列数据检测差异表达中的应用

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摘要

An important problem in microarray experiments is the detection of genes that are differentially expressed in agiven mumber of classes. We consider a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. By converting to a z-score the value of the test statistic used to test the significance of each gene, we can use a simple two-component normal mixture to model adequately the distribution of this score. In the context of the application of this approach to a well known breast cancer data set, we consider some of the issues associated with the problem of the detection of differential expression, including the case where there is need for the use of an empirical null distribution in place of the standard normal (the theoretical null) and the case where none of the genes might be differentially expressed. We also describe briefly some initial results on a cluster analysis approach to this problem, which attempts to model the joint distribution of the individual gene expressions. This latter approach thus has to make distributional assumptions which are note necessary with the former approach based on the z-scores. However, in the case where the distributional assumptions are valid, it has the potential to provide a more powerful analysis.
机译：微阵列实验中的一个重要问题是检测在给定类中差异表达的基因。我们考虑一种简单易行的方法来估算单个基因为空的后验概率。该问题可以使用经验贝叶斯方法在两成分混合框架中表示。当前实现此方法的方法由于做出的最小假设而有一些局限性，或者更具体的假设是计算密集型的。通过将用于检验每个基因的显着性的检验统计量的值转换为z分数，我们可以使用简单的两成分正态混合物来充分模拟该得分的分布。在将此方法应用于众所周知的乳腺癌数据集的背景下，我们考虑了与差异表达检测问题相关的一些问题，包括需要使用经验空分布的情况代替标准正常值（理论上为空）和所有基因均不可以差异表达的情况。我们还简要描述了针对此问题的聚类分析方法的一些初步结果，该方法试图对单个基因表达的联合分布进行建模。因此，后一种方法必须做出分布假设，这是基于z得分的前一种方法所必需的。但是，在分布假设有效的情况下，它有可能提供更强大的分析。

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    McLachlan G.; Wang K.; Ng S.;

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  • 年度 2008
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  • 正文语种 English
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