It has been shown that the generalized F-statistics can give satisfactory performances in identifying differentially expressed genes with microarray data. However, for some complex diseases, it is still possible to identify a high proportion of false positives because of the modest differential expressions of disease related genes and the systematic noises of microarrays. The main purpose of this study is to develop statistical methods for Affymetrix microarray gene expression data so that the impact on false positives from non-expressed genes can be reduced. I proposed two novel generalized F-statistics for identifying differentially expressed genes and a novel approach for estimating adjusting factors. The proposed statistical methods systematically combine filtering of non-expressed genes and identification of differentially expressed genes. For comparison, the discussed statistical methods were applied to an experimental data set for a type 2 diabetes study. In both two- and three-sample analyses, the proposed statistics showed improvement on the control of false positives.

译文

已经表明,广义F统计量可以在用微阵列数据鉴定差异表达基因方面提供令人满意的性能。然而,对于一些复杂的疾病,由于疾病相关基因的适度差异表达和微阵列的系统噪声,仍然有可能识别出高比例的假阳性。这项研究的主要目的是开发Affymetrix微阵列基因表达数据的统计方法,以便可以减少对非表达基因假阳性的影响。我提出了两种新颖的通用F统计量来识别差异表达的基因,以及一种估计调整因子的新方法。所提出的统计方法系统地结合了非表达基因的过滤和差异表达基因的鉴定。为了进行比较,将讨论的统计方法应用于2型糖尿病研究的实验数据集。在两样本和三样本分析中,建议的统计数据均显示出对假阳性的控制有所改善。

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