A flexible and powerful bayesian hierarchical model for ChIP-chip experiments
A flexible and powerful bayesian hierarchical model for ChIP-chip experiments
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DOI：
10.1111/j.1541-0420.2007.00899.x
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发表时间：
2008-06-01
期刊：
影响因子：
1.9
通讯作者：
Liu, X. Shirley
中科院分区：
文献类型：
--
作者：
Gottardo, Raphael;Li, Wei;Liu, X. Shirley
染色质免疫沉淀微阵列（ChIP-chip）使研究人员能够识别特定dna结合蛋白结合的特定基因组区域，由于探针数量多，噪声与信号比高，探针之间的空间依赖性，这给统计分析带来了新的挑战。我们提出了一种称为BAC （ChIP-chip的贝叶斯分析）的方法来检测转录因子结合区域，该方法结合了探针之间的依赖性，同时对结合区域（例如，长度）做了很少的假设。BAC对于探测具有可交换先验方差的异常值具有鲁棒性，这允许探测的不同方差，但仍然缩小极端经验方差。采用马尔可夫链蒙特卡罗进行参数估计，并基于参数的联合分布进行推理。结合区域检测使用后验概率计算从相邻探针的联合后验分布。我们表明，这些后验概率是很好的校准，可以用来获得错误发现率的估计。该方法使用两个公开可用的ChIP-chip数据集进行说明，其中包含18个实验验证的区域。我们将我们的方法与其他四种基线和常用技术进行了比较，即Wilcoxon秩和检验、TileMap、HGMM和MAT。我们发现BAC和HGMM在检测验证区域方面表现最好。然而，与BAC相比，HGMM似乎对探测异常值非常敏感。此外，我们提出了一项仿真研究，表明BAC在各种仿真场景下比其他四种技术更强大，同时对模型错误规范具有鲁棒性。
Chromatin-immunoprecipitation microarrays (ChIP-chip) that enable researchers to identify regions of a given genome that are bound by specific DNA-binding proteins present new challenges for statistical analysis due to the large number of probes, the high noise-to-signal ratio, and the spatial dependence between probes. We propose a method called BAC (Bayesian analysis of ChIP-chip) to detect transcription factor bound regions, which incorporate the dependence between probes while making little assumptions about the bound regions (e.g., length). BAC is robust to probe outliers with an exchangeable prior for the variances, which allows different variances for the probes but still shrink extreme empirical variances. Parameter estimation is carried out using Markov chain Monte Carlo and inference is based on the joint distribution of the parameters. Bound regions are detected using posterior probabilities computed from the joint posterior distribution of neighboring probes. We show that these posterior probabilities are well calibrated and can be used to obtain an estimate of the false discovery rate. The method is illustrated using two publicly available ChIP-chip data sets containing 18 experimentally validated regions. We compare our method to four other baseline and commonly used techniques, namely, the Wilcoxon's rank sum test, TileMap, HGMM, and MAT. We found BAC and HGMM to perform best at detecting validated regions. However, HGMM appears to be very sensitive to probe outliers compared to BAC. In addition, we present a simulation study, which shows that BAC is more powerful than the other four techniques under various simulation scenarios while being robust to model misspecification.