Gene set analysis aims to identify differentially expressed or co-expressed genes within a biological pathway between two experimental conditions, so that it can eventually reveal biological processes and pathways involved in disease development. In the last few decades, various statistical and computational methods have been proposed to improve statistical power of gene set analysis. In recent years, much attention has been paid to differentially co-expressed genes since they can be potentially disease-related genes without significant difference in average expression levels between two conditions. In this paper, we propose a new statistical method to identify differentially co-expressed genes from microarray gene expression data. The proposed method first estimates co-expression levels of paired genes using covariance regularization by thresholding, and then significance of difference in covariance estimation between two conditions is evaluated. We demonstrated that the proposed method is more powerful than the existing main-stream methods to detect co-expressed genes through extensive simulation studies. Also, we applied it to various microarray gene expression datasets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer.

中文翻译：

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从微阵列基因表达数据中检测差异共表达基因的协方差阈值

基因集分析旨在识别两种实验条件之间的生物通路中差异表达或共表达的基因，从而最终揭示疾病发展中涉及的生物过程和通路。在过去的几十年中，已经提出了各种统计和计算方法来提高基因集分析的统计能力。近年来，差异共表达基因受到了很多关注，因为它们可能是潜在的疾病相关基因，而两种情况之间的平均表达水平没有显着差异。在本文中，我们提出了一种新的统计方法，用于从微阵列基因表达数据中识别差异共表达基因。所提出的方法首先通过阈值化使用协方差正则化估计配对基因的共表达水平，然后评估两个条件之间协方差估计的差异的显着性。我们证明了所提出的方法比现有的主流方法更强大，通过广泛的模拟研究来检测共表达基因。此外，我们将其应用于与突变 p53 转录活性以及上皮和间质乳腺癌相关的各种微阵列基因表达数据集。