Bayesian Models for Gene Expression with Microarray Data

描述(申请人提供)：这个项目涉及基因表达数据的参数和半参数建模。DNA微阵列和其他用于分析复杂核酸序列的高通量方法现在使快速、高效和准确地测量生物样本中表达的许多基因的水平成为可能。微阵列数据分析的主要困难是，与问题的维度(基因数量)相比，样本大小非常小。单个个体的基因数量通常在数千个，而数据集中的个体很少。针对基因选择、肿瘤分类、贝叶斯网络、基因聚类和降维方法，我们提出了几种新的参数贝叶斯建模方法。现有的大多数方法不是以模型为基础的，因此不能处理关于正式评估不确定性或评估特定模型的适合性的具体问题。此外，基于模型的方法还提供了扩展到更复杂情况的可能性，例如，概率混合建模、处理丢失数据等。我们将开发用于微阵列数据的贝叶斯分层模型，该模型将在不同的水平上灵活地容纳几个建模因素。在几个建模框架中，我们将使模型空间的维度保持未知，以增加灵活性。在这些灵活的模型类中不可能得到解析解，因此基于仿真的马尔科夫链蒙特卡罗(MCMC)方法和跳维算法将被用来推导未知参数的估计(不确定性分布)。

DESCRIPTION (provided by applicant): This project is concerned with parametric and semiparametric modeling of gene expression data. DNA microarrays and other high-throughput methods for analyzing complex nucleic acid sequences now make it possible to rapidly, efficiently and accurately measure the levels of many genes expressed in a biological sample. The main difficulty with microarray data analysis is that the sample size is very small when compared to the dimension of the problem (the number of genes). The number of genes for a single individual is usually in the thousands and there are few individuals in the data set. We propose several novel parametric Bayesian modeling approaches for gene selection, tumor classification, Bayesian networks, gene clustering and dimension reduction methods. Most of the existing methods are not model-based and thus are unable to address specific questions regarding formal assessment of uncertainties or assessment of the fit of a specific model. Also model-based approaches offer the potential for extension to more complex situations, e.g., probabilistic mixture modeling, handling missing data, etc. We will develop Bayesian hierarchical models for microarray data, which will accommodate several modeling factors flexibly at different levels. In several of the modeling frameworks, we will keep the dimension of the model space unknown to create added flexibility. It is impossible to get analytical answers in these flexible classes of models so simulation based Markov Chain Monte Carlo (MCMC) methodology with dimensional jumping algorithms will be used to derive the estimates (uncertainty distributions) of the unknown parameters.