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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Covariance thresholding to detect differentially co-expressed genes from microarray gene expression data
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Covariance thresholding to detect differentially co-expressed genes from microarray gene expression data

机译：从微阵列基因表达数据检测差异共表达基因的协方差

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摘要

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.
机译：基因设定分析旨在在两个实验条件下鉴定生物途径内的差异表达或共同表达基因，因此它最终可以揭示疾病发展中涉及的生物过程和途径。在过去的几十年中，已经提出了各种统计和计算方法来改善基因集分析的统计力量。近年来，已经注意到差异共同表达的基因很多，因为它们可以是潜在的疾病相关的基因，而两个条件之间的平均表达水平显着差异。在本文中，我们提出了一种新的统计学方法，用于鉴定微阵列基因表达数据的差异共表达基因。所提出的方法首先通过阈值化估计使用协方差规范化的配对基因的共表达水平，然后评估两个条件之间的协方差估计差异的显着性。我们证明，通过广泛的模拟研究，所提出的方法比现有的主要流动方法更强大，以检测共同表达基因。此外，我们将其应用于与突变体P53转录活性相关的各种微阵列基因表达数据集，以及上皮和基质乳腺癌。

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