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Significance Analysis and Improved Discovery of Differentially Co-expressed Gene Sets in Microarray Data

机译:微阵列数据中差异共表达基因集的重要性分析及改进的发现

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Differential co-expression signifies the deregulated pathways as opposed to differential expression that signifies change of gene expression. Kostka and Spang proposed a score and an algorithm to elicit differentially co-expressed gene-sets. We analyze the statistical properties of their score in two different data processing settings and obtain respective null-distributions to provide the statistical significance of a gene-set through the p-value of its score. We propose to use these p-values to automate their algorithm. In addition, we propose a two stage algorithm, based on Friendly Neighbors (FNs) algorithm, called FNs-KS algorithm for improved discovery of such gene set i.e. improves both sensitivity and specificity of the discovery.
机译:差分共表达意味着解毒的途径与差异表达表示,表示基因表达的变化。 Kostka和Spang提出了分数和算法,以引发差异共同表达的基因集。我们分析了两种不同的数据处理设置中得分的统计特性,得到了各自的零分布,以通过其分数的P值提供基因集的统计学意义。我们建议使用这些p值来自动化其算法。此外,我们提出了一种基于友好邻居(FNS)算法的两个阶段算法,称为FNS-KS算法,用于改进此类基因的发现I.。提高了发现的敏感性和特异性。

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