Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models.
Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models.
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DOI：
10.1002/sim.5658
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发表时间：
2013-06-15
影响因子：
2
通讯作者：
Xiao, Guanghua
中科院分区：
文献类型：
--
作者：
Wang, Xinlei;Zang, Miao;Xiao, Guanghua
关键词：
表观遗传学是研究基因组的变化，可以打开或关闭基因，并确定哪些蛋白质被转录而不改变DNA序列。最近，表观遗传学的变化已被链接到疾病的发展和进展，如精神疾病。高通量表观遗传实验使研究人员能够测量全基因组表观遗传谱，并产生由免疫沉淀与参考样品的强度比组成的数据。强度比可以提供在一个实验条件下发生蛋白质结合的基因组区域的视图，并进一步允许我们通过两个不同条件之间的比较来检测表观遗传改变。然而，这样的实验可能是昂贵的，只有几个重复可用。此外，表观遗传数据往往与高噪声水平的空间相关。在本文中，我们开发了一个贝叶斯层次模型，结合隐马尔可夫过程与四个国家的空间依赖性建模，检测基因组位点的表观遗传变化的双样本实验与配对的内部控制。所提出的方法的一个有吸引力的特点是，隐马尔可夫过程的四个状态有明确的生物意义，并允许我们直接调用相应的后验概率的基础上的变化模式。相比之下，现有的方法都不能提供这种优势。此外，所提出的方法通过空间平滑（通过隐马尔可夫建模）和信息池（通过分层建模）在统计推断中提供了强大的功能。模拟研究和可卡因成瘾研究中的真实的数据分析都说明了这种方法的可靠性和成功性。
Epigenetics is the study of changes to the genome that can switch genes on or off and determine which proteins are transcribed without altering the DNA sequence. Recently, epigenetic changes have been linked to the development and progression of disease such as psychiatric disorders. High-throughput epigenetic experiments have enabled researchers to measure genome-wide epigenetic profiles and yield data consisting of intensity ratios of immunoprecipitation versus reference samples. The intensity ratios can provide a view of genomic regions where protein binding occur under one experimental condition and further allow us to detect epigenetic alterations through comparison between two different conditions. However, such experiments can be expensive, with only a few replicates available. Moreover, epigenetic data are often spatially correlated with high noise levels. In this paper, we develop a Bayesian hierarchical model, combined with hidden Markov processes with four states for modeling spatial dependence, to detect genomic sites with epigenetic changes from two-sample experiments with paired internal control. One attractive feature of the proposed method is that the four states of the hidden Markov process have well-defined biological meanings and allow us to directly call the change patterns based on the corresponding posterior probabilities. In contrast, none of existing methods can offer this advantage. In addition, the proposed method offers great power in statistical inference by spatial smoothing (via hidden Markov modeling) and information pooling (via hierarchical modeling). Both simulation studies and real data analysis in a cocaine addiction study illustrate the reliability and success of this method.
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影响因子：
2.1
作者：
Newton, MA;Noueiry, A;Ahlquist, P
通讯作者：
Ahlquist, P
DOI：
10.2202/1544-6115.1692
发表时间：
2011-01-01
影响因子：
0.9
作者：
Berard，Caroline;Martin-Magniette，Marie-Laure;Robin，Stephane
通讯作者：
Robin，Stephane
影响因子：
1.9
作者：
Gottardo, Raphael;Li, Wei;Liu, X. Shirley
通讯作者：
Liu, X. Shirley
影响因子：
16.2
作者：
Kawabe, Hiroshi;Neeb, Antje;Dimova, Kalina;Young, Samuel M., Jr.;Takeda, Michiko;Katsurabayashi, Shutaro;Mitkovski, Miso;Malakhova, Oxana A.;Zhang, Dong-Er;Ulmilkawa, Masato;Kariya, Ken-ichi;Goebbels, Sandra;Nave, Klaus-Armin;Rosenmund, Christian;Jahn, Olaf;Rhee, JeongSeop;Brose, Nils
通讯作者：
Brose, Nils
影响因子：
2.2
作者：
Brooks, SP;Roberts, GO
通讯作者：
Roberts, GO