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首页> 外文期刊>Genomics >Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes.
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Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes.

机译：微阵列数据的排名分析：一种鉴定差异表达基因的有力方法。

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摘要

Microarray technology provides a powerful tool for the expression profile of thousands of genes simultaneously, which makes it possible to explore the molecular and metabolic etiology of the development of a complex disease under study. However, classical statistical methods and technologies fail to be applicable to microarray data. Therefore, it is necessary and motivating to develop powerful methods for large-scale statistical analyses. In this paper, we described a novel method, called Ranking Analysis of Microarray Data (RAM). RAM, which is a large-scale two-sample t-test method, is based on comparisons between a set of ranked T statistics and a set of ranked Z values (a set of ranked estimated null scores) yielded by a "randomly splitting" approach instead of a "permutation" approach and a two-simulation strategy for estimating the proportion of genes identified by chance, i.e., the false discovery rate (FDR). The results obtained from the simulated and observed microarray data show that RAM is more efficient in identification of genes differentially expressed and estimation of FDR under undesirable conditions such as a large fudge factor, small sample size, or mixture distribution of noises than Significance Analysis of Microarrays.
机译：微阵列技术为同时表达数千种基因提供了强大的工具，这使得探索正在研究的复杂疾病发展的分子和代谢病因成为可能。但是，经典的统计方法和技术无法应用于微阵列数据。因此，有必要并且有动机开发用于大规模统计分析的强大方法。在本文中，我们描述了一种新颖的方法，称为芯片数据排序分析（RAM）。 RAM是一种大规模的两样本t检验方法，它基于一组“ T分布统计量”与一个“随机分裂”所产生的一组Z值（一组估算的空值）之间的比较。该方法代替“置换”方法和两种模拟策略，用于估计偶然发现的基因比例，即错误发现率（FDR）。从仿真和观察到的微阵列数据获得的结果表明，与微阵列的意义分析相比，RAM在不希望的条件下（如软糖因子大，样本量小或噪声的混合分布）在识别差异表达的基因和估计FDR方面更有效。 。

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