Bayesian Mixture Modeling of Functional Genomics Data

描述(由申请人提供)：本提案的目标是开发数学框架和相应的计算工具，用于识别功能基因组数据中具有统计学意义的模式。对于识别微阵列数据和其他复杂生物数据集中的共表达基因组，聚类分析一直是一种有效的方法。这些分析的结果有助于剖析驱动共表达的调控机制，确定参与生物过程的途径，并从功能上注释基因。这些结果和结论的质量直接关系到分析中使用的聚类过程的质量。目前使用的集群分析计算工具在评估集群结果的统计意义、跨不同研究和生物系统的数据集群以及在分析中整合其他数据类型方面存在不足。我们提出了一项多学科研究，以扩展贝叶斯无限混合的数学框架，并开发相关的计算工具。在目前可用的聚类方法中，贝叶斯无限混合是独一无二的，因为它们能够以最佳方式利用数据中的信息，并考虑到包含在聚类分析结果中的所有不确定源。到目前为止，在我们的实验中，基于无限混合模型的聚类过程优于使用模拟数据和真实世界微阵列数据集的所有替代方法。拟议的扩展将促进跨不同研究和生物系统的“元聚类”，并将在分析中整合背景知识和其他数据类型。相应的计算程序将通过模拟研究、对公开数据的分析和对乳腺肿瘤启动的研究来验证。所有方法都将经过优化，并通过BioConductor包作为独立的命令行程序和源代码本身交付给科学界。使用这些计算程序的生物医学研究人员将显著提高他们正确解释微阵列实验结果的能力。

DESCRIPTION (provided by applicant): The objective of this proposal is to develop mathematical framework and corresponding computational tools for identifying statistically significant patterns in functional genomics data. Cluster analysis has been a productive approach for identifying groups of co-expressed genes in microarray data and other complex biological data sets. Results of such analyses have served to dissect regulatory mechanisms driving co- expression, identify pathways involved in biological processes and functionally annotate genes. The quality of these results and conclusions are directly related to the quality of the clustering procedure used in the analysis. Currently used computational tools for cluster analysis are inadequate with respect to assessing the statistical significance of clustering results, clustering data across different studies and biological systems, and integrating additional data types in the analysis. We propose a multidisciplinary study to extend the mathematical framework of Bayesian infinite mixtures and to develop related computational tools. Bayesian infinite mixtures are unique among currently available clustering approaches in their ability to optimally use the information in the data and to account for all sources of uncertainty that are incorporated in results of a cluster analysis. In our experiments thus far, clustering procedures based on infinite mixture models outperformed all alternative approaches with both simulated data and real-world microarray datasets. Proposed extensions will facilitate "meta-clustering" across different studies and biological systems and will integrate background knowledge and additional data types in the analysis. Corresponding computational procedures will be validated through simulation studies, analysis of publicly available data and a study of mammary tumor initiation. All methods will be optimized and delivered to the scientific community through a Bioconductor package, as a stand-alone command-line program, and the source code itself. Biomedical researchers using these computational procedures will significantly improve their ability to correctly interpret results of their microarray experiments.