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Significance analysis and improved discovery of disease-specific differentially co-expressed gene sets in microarray data

机译:芯片数据中疾病特异性差异共表达基因集的意义分析和改进发现

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Kostka and Spang proposed a statistic (KS-statistic) and an algorithm (KS algorithm) to elicit Differentially Co-expressed Gene Sets (DCEGSs) by minimising KS-statistic. We prove that the statistical distributions of KS-statistic under null hypothesis in variance un-normalised and normalised data settings are central and doubly non-central F-distributions, respectively. Based on this analysis, we propose two alternative but equivalent statistics whose null distributions are easier to evaluate. Further, we propose to improve the algorithm by objectively setting the search parameters via maximising the statistical significance of the resultant gene set and pre-filtering the genes by Friendly Neighbours (FNs) algorithm.
机译:Kostka和Spang提出了一种统计量(KS-statistic)和一种算法(KS算法),以通过最小化KS-statistic来引发差异共表达基因集(DCEGS)。我们证明了在零假设下方差未归一化和归一化数据设置下的KS统计量的统计分布分别是中心F分布和双重非中心F分布。基于此分析,我们提出了两个替代的但等价的统计量,它们的零值分布更易于评估。此外,我们建议通过最大程度地提高所得基因集的统计显着性并通过友好邻居(FNs)算法对基因进行预过滤来客观地设置搜索参数来改进算法。

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