Mixture modeling for genome-wide localization of transcription factors
Mixture modeling for genome-wide localization of transcription factors
复制标题
DOI：
10.1111/j.1541-0420.2005.00659.x
复制
发表时间：
2007-03-01
期刊：
影响因子：
1.9
通讯作者：
Keles, Sunduz
中科院分区：
文献类型：
--
作者：
Keles, Sunduz
染色质免疫沉淀和DNA微阵列分析（ChIP芯片方法）是全基因组蛋白质-DNA相互作用作图的有效方法。数据前端平铺阵列包括对平铺整个染色体或基因组的数千或数百万个短寡核苷酸（探针）的DNA-蛋白质相互作用测量。我们提出了一种新的基于模型的方法来分析ChIP芯片数据。该模型的动机是广泛使用的二组分多项式混合模型从头模体发现。它利用结合强度的分层伽马混合模型，同时结合数据的固有空间结构。在该模型中，基因组区域属于以下两个一般组中的任一个：具有局部蛋白质-DNA相互作用（峰）的区域和缺乏这种相互作用的区域。允许基因组区域内的各个探针具有不同的定位速率，以适应不同的结合亲和力。该模型的一个新的特点是纳入了来自实验设计和参数的峰大小的分布。这导致放松固定的峰值大小的假设，这是通常采用时，计算这些类型的空间数据的检验统计量。模拟研究和真实的数据应用表明，该方法具有良好的操作特性，包括与可用的替代方法相比，具有小样本量的高灵敏度。
Chromatin immunoprecipitation followed by DNA microarray analysis (ChIP-chip methodology) is ail efficient way of mapping genome-wide protein-DNA interactions. Data front tiling arrays encompass DNA-protein interaction measurements on thousands or millions of short oligonucleotides (probes) tiling a whole chromosome or genome. We propose a new model-based method for analyzing ChIP-chip data. The proposed model is motivated by the widely used two-component multinomial mixture model of de novo motif finding. It utilizes a hierarchical gamma mixture model of binding intensities while incorporating inherent spatial structure of the data. In this model, genomic regions belong to either one of the following two general groups: regions with a local protein-DNA interaction (peak) and regions lacking this interaction. Individual probes within a genomic region are allowed to have different localization rates accommodating different binding affinities. A novel feature of this model is the incorporation of a distribution for the peak size derived from the experimental design and parameters. This leads to the relaxation of the fixed peak size assumption that is commonly employed when computing a test statistic for these types of spatial data. Simulation studies and a real data application demonstrate good operating characteristics of the method including high sensitivity with small sample sizes when compared to available alternative methods.