Kuan Pei Fen, Chun Hyonho, Keleş Sündüz
Department of Statistics, 1300 University Avenue, University of Wisconsin, Madison, WI 53706, USA.
Pac Symp Biocomput. 2008:515-26.
Whole genome tiling arrays at a user specified resolution are becoming a versatile tool in genomics. Chromatin immunoprecipitation on microarrays (ChIP-chip) is a powerful application of these arrays. Although there is an increasing number of methods for analyzing ChIP-chip data, perhaps the most simple and commonly used one, due to its computational efficiency, is testing with a moving average statistic. Current moving average methods assume exchangeability of the measurements within an array. They are not tailored to deal with the issues due to array designs such as overlapping probes that result in correlated measurements. We investigate the correlation structure of data from such arrays and propose an extension of the moving average testing via a robust and rapid method called CMARRT. We illustrate the pitfalls of ignoring the correlation structure in simulations and a case study. Our approach is implemented as an R package called CMARRT and can be used with any tiling array platform.
以用户指定分辨率的全基因组平铺阵列正成为基因组学中一种通用工具。微阵列染色质免疫沉淀技术（ChIP-chip）是这些阵列的一项强大应用。尽管分析ChIP-chip数据的方法越来越多，但由于其计算效率，或许最简单且最常用的方法是使用移动平均统计量进行检验。当前的移动平均方法假定阵列内测量值具有可交换性。它们并非专门为处理因阵列设计（如导致相关测量的重叠探针）而产生的问题而设计。我们研究了此类阵列数据的相关结构，并通过一种名为CMARRT的稳健且快速的方法提出了移动平均检验的扩展。我们在模拟和一个案例研究中说明了忽略相关结构的陷阱。我们的方法实现为一个名为CMARRT的R包，可用于任何平铺阵列平台。
