组学应用中自适应加权Fisher荟萃分析方法的P值评估、变异性指数和生物标志物分类
P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher's meta-analysis method in omics applications
摘要：
荟萃分析方法已被广泛用于结合多项临床或基因组研究的结果，以增加统计功效并确保稳健和准确的结论。自适应加权Fisher方法（AW-Fisher）是一种有效的方法，可结合来自$K$独立研究的p值，并通过表征哪些研究有助于荟萃分析来提供更好的生物学解释。目前，AW-Fisher缺乏快速、准确的p值计算和AW权重的变异性估计。当研究$K$的数量很大时，$3^K - 1$可能的差异表达模式类别可能变得难以处理。在本文中，我们应用重要性抽样技术与样条插值，以提高精度和速度的p值计算。使用reservation技术，我们提出了一个变异指数的AW权重估计和共同隶属度矩阵来表征基因之间的模式相似性。共同隶属度矩阵进一步用于基于差异表达基因的元模式对差异表达基因进行分类，以用于进一步的生物学研究。所提出的方法的优越性能在模拟中示出。这些方法也适用于两个实际的应用程序，以证明有趣的生物学发现。
英文摘要：
Meta-analysis methods have been widely used to combine results from multiple clinical or genomic studies to increase statistical power and ensure robust and accurate conclusion. Adaptively weighted Fisher's method (AW-Fisher) is an effective approach to combine p-values from $K$ independent studies and to provide better biological interpretation by characterizing which studies contribute to meta-analysis. Currently, AW-Fisher suffers from lack of fast, accurate p-value computation and variability estimate of AW weights. When the number of studies $K$ is large, the $3^K - 1$ possible differential expression pattern categories can become intractable. In this paper, we apply an importance sampling technique with spline interpolation to increase accuracy and speed of p-value calculation. Using resampling techniques, we propose a variability index for the AW weight estimator and a co-membership matrix to characterize pattern similarities between genes. The co-membership matrix is further used to categorize differentially expressed genes based on their meta-patterns for further biological investigation. The superior performance of the proposed methods is shown in simulations. These methods are also applied to two real applications to demonstrate intriguing biological findings.
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