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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Identification of Differentially Expressed Genes for Time-Course Microarray Data Based on Modified RM ANOVA
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Identification of Differentially Expressed Genes for Time-Course Microarray Data Based on Modified RM ANOVA

机译：基于改进的RM ANOVA的时程微阵列数据差异表达基因的鉴定

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摘要

The regulation of gene expression is a dynamic process, hence it is of vital interest to identify and characterize changes in gene expression over time. We present here a general statistical method for detecting changes in microarray expression over time within a single biological group and is based on repeated measures (RM) ANOVA. In this method, unlike the classical F-statistic, statistical significance is determined taking into account the time dependency of the microarray data. A correction factor for this RM F-statistic is introduced leading to a higher sensitivity as well as high specificity. We investigate the two approaches that exist in the literature for calculating the p-values using resampling techniques of gene-wise p-values and pooled p-values. It is shown that the pooled p-values method compared to the method of the gene-wise p-values is more powerful, and computationally less expensive, and hence is applied along with the introduced correction factor to various synthetic data sets and a real data set. These results show that the proposed technique outperforms the current methods. The real data set results are consistent with the existing knowledge concerning the presence of the genes. The algorithms presented are implemented in R and are freely available upon request.
机译：基因表达的调节是一个动态的过程，因此，随着时间的推移，鉴定和表征基因表达的变化至关重要。我们在此提出一种用于检测单个生物组内微阵列表达随时间变化的一般统计方法，该方法基于重复测量（RM）方差分析。在这种方法中，与传统的F统计量不同，统计显着性是根据微阵列数据的时间依赖性来确定的。引入了针对该RM F统计量的校正因子，从而导致更高的灵敏度和更高的特异性。我们调查了文献中存在的两种使用基因方式p值和合并p值的重采样技术计算p值的方法。结果表明，与基于基因的p值方法相比，合并的p值方法功能更强大，并且计算成本更低，因此与引入的校正因子一起应用于各种合成数据集和真实数据组。这些结果表明，所提出的技术优于现有方法。真实的数据集结果与有关基因存在的现有知识一致。提出的算法在R中实现，并可根据要求免费提供。

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