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首页> 外文期刊>The Annals of applied statistics >BAYESIAN HIDDEN MARKOV TREE MODELS FOR CLUSTERING GENES WITH SHARED EVOLUTIONARY HISTORY
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BAYESIAN HIDDEN MARKOV TREE MODELS FOR CLUSTERING GENES WITH SHARED EVOLUTIONARY HISTORY

机译：贝叶斯隐马尔可夫树模型用于共同进化历史的聚类基因

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摘要

Determination of functions for poorly characterized genes is crucial for understanding biological processes and studying human diseases. Functionally associated genes are often gained and lost together through evolution. Therefore identifying co-evolution of genes can predict functional gene-gene associations. We describe here the full statistical model and computational strategies underlying the original algorithm CLustering by Inferred Models of Evolution (CLIME 1.0) recently reported by us (Cell 158 (2014) 213-225). CLIME 1.0 employs a mixture of tree-structured hidden Markov models for gene evolution process, and a Bayesian model-based clustering algorithm to detect gene modules with shared evolutionary histories (termed evolutionary conserved modules, or ECMs). A Dirichlet process prior was adopted for estimating the number of gene clusters and a Gibbs sampler was developed for posterior sampling. We further developed an extended version, CLIME 1.1, to incorporate the uncertainty on the evolutionary tree structure. By simulation studies and benchmarks on real data sets, we show that CLIME 1.0 and CLIME 1.1 outperform traditional methods that use simple metrics (e.g., the Hamming distance or Pearson correlation) to measure co-evolution between pairs of genes.
机译：表征差异差的基因的功能对于了解生物过程和研究人类疾病至关重要。功能相关的基因通常通过进化获得和丢失在一起。因此，鉴定基因的共同演化可以预测功能基因基因关联。我们在这里描述了通过我们最近报告的进化模型（Cell 158（2014）213-225）推断出原始算法集群的全统计模型和计算策略。 Clime 1.0使用用于基因演进过程的树结构隐马尔可夫模型的混合，以及贝叶斯模型的聚类算法，用于检测具有共用进化历史的基因模块（称为进化保守模块或ECMS）。采用Dirichlet工艺用于估计基因簇的数量和Gibbs采样器进行后部抽样。我们进一步开发了扩展版本，Clime 1.1，将不确定性纳入进化树结构。通过仿真研究和实际数据集的基准，我们展示了Clime 1.0和Clime 1.1优于使用简单度量（例如，汉明距离或Pearson相关）来测量基因对之间的共同演变的传统方法。

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