Blangiardo Marta, Richardson Sylvia
Centre for Biostatistics, Imperial College, St Mary's Campus, Norfolk Place, London, UK.
Genome Biol. 2007;8(4):R54. doi: 10.1186/gb-2007-8-4-r54.
We propose a novel approach for finding a list of features that are commonly perturbed in two or more experiments, quantifying the evidence of dependence between the experiments by a ratio. We present a Bayesian analysis of this ratio, which leads us to suggest two rules for choosing a cut-off on the ranked list of p values. We evaluate and compare the performance of these statistical tools in a simulation study, and show their usefulness on two real datasets.
我们提出了一种新颖的方法，用于找出在两个或更多实验中普遍受到干扰的一系列特征，并通过一个比率来量化实验之间的相关性证据。我们对这个比率进行了贝叶斯分析，这使我们提出了两条规则，用于在p值排名列表上选择一个截止值。我们在模拟研究中评估并比较了这些统计工具的性能，并在两个真实数据集上展示了它们的实用性。
