一种选择基因和基因子网络的贝叶斯非参数混合模型
A Bayesian nonparametric mixture model for selecting genes and gene subnetworks
摘要：
在高通量数据分析中，从成千上万的测量特征中选择信息特征是非常具有挑战性的。最近，已经开发了几个参数/回归模型，利用基因网络信息来选择与临床/生物学结果密切相关的基因或途径。另外，在本文中，我们提出了一个非参数贝叶斯模型的基因选择网络信息。除了识别与临床结果有很强关联的基因外，我们的模型还可以选择具有特定表达行为的基因，在这种情况下，回归模型不直接适用。我们表明，我们提出的模型是等价的无穷混合模型，我们开发了一个后验计算算法的基础上马尔可夫链蒙特卡罗（MCMC）方法。我们还提出了两个快速计算算法，近似后验模拟具有良好的精度，但相对较低的计算成本。我们说明了我们的模拟研究和斯派曼酵母细胞周期微阵列数据的分析方法。
英文摘要：
It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.
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