A distance-based test of association between paired heterogeneous genomic data
A distance-based test of association between paired heterogeneous genomic data
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DOI：
10.1093/bioinformatics/btt450
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发表时间：
2013-10-15
期刊：
影响因子：
5.8
通讯作者：
Montana, Giovanni
中科院分区：
文献类型：
--
作者：
Minas, Christopher;Curry, Edward;Montana, Giovanni
动机：由于快速的技术进步，可以从给定的生物样品获得广泛的不同测量，包括单核苷酸多态性、拷贝数变异、基因表达水平、DNA甲基化和蛋白质组学谱。这些不同的测量提供了表征生物多样性的某个方面的手段，并且广泛关注的基本问题涉及发现不同数据类型之间的共享变异模式。这样的数据类型是异构的，在这个意义上，他们代表在不同的尺度或不同的数据结构所表示的测量结果：我们提出了一个基于距离的统计测试，广义RV（GRV）测试，以评估是否有一个共同的和非随机的模式从同一随机样本中获得的配对生物测量之间的变异。测量值通过使用两个距离测量值进入测试，可以选择这两个距离测量值来捕获数据的特定方面。提出了一种近似零分布来计算封闭形式的P值，而不需要执行昂贵的Monte Carlo置换程序。与经典的距离矩阵之间的关联的Mantel测试相比，GRV测试已被发现在一些模拟设置中更强大。我们还展示了如何GRV测试可用于检测生物学途径，其中遗传变异性与卵巢癌样本中基因表达水平的变化相关，并提供了从两个独立队列中获得的结果。
Motivation: Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diversity, and a fundamental problem of broad interest concerns the discovery of shared patterns of variation across different data types. Such data types are heterogeneous in the sense that they represent measurements taken at different scales or represented by different data structures.Results: We propose a distance-based statistical test, the generalized RV (GRV) test, to assess whether there is a common and non-random pattern of variability between paired biological measurements obtained from the same random sample. The measurements enter the test through the use of two distance measures, which can be chosen to capture a particular aspect of the data. An approximate null distribution is proposed to compute P-values in closed-form and without the need to perform costly Monte Carlo permutation procedures. Compared with the classical Mantel test for association between distance matrices, the GRV test has been found to be more powerful in a number of simulation settings. We also demonstrate how the GRV test can be used to detect biological pathways in which genetic variability is associated to variation in gene expression levels in an ovarian cancer sample, and present results obtained from two independent cohorts.