Methods for genomic data with graphical structures

描述(由申请人提供)：该项目广泛的、长期的目标涉及开发新的统计方法和计算工具，用于对由重要生物学问题和实验驱动的大规模多基因组数据进行统计和概率建模。新的高通量技术和下一代测序正在产生各种类型的非常高维的基因组和蛋白质组数据和元数据(例如，网络和通路数据库)，以获得对各种复杂表型的系统级理解。随着数据的数量和复杂性的增加，以及所解决的问题变得更加复杂，需要统计分析方法来整合这些基因组数据，同时可以将关于基因功能和途径的信息纳入数字载体/矩阵数据的分析中，以便得出有效的统计和生物学推断。目前项目的具体目标是开发新的统计模型和方法，以便在路径和网络的背景下对基因组数据进行综合分析。基于对遗传基因组数据和不同癌症基因组数据的分析，第一个目标是开发新的统计方法，在转录水平上估计一组基因的基因调整后的精度矩阵。由此得到的回归系数矩阵和稀疏精度矩阵为调整顺式和反式基因表达的基因调控提供了重要的信息。第二个目标是建立用于eQTL数据分析的高维工具变量回归，以便识别以全基因组基因型为工具变量的表型的潜在原因基因。AIMS 3和4提出了一套新的基因集丰富分析方法，包括通过检验协方差矩阵的齐性来进行基因集分析的方法，以及一类用于综合分析不同基因组数据的多变量随机集方法。这些方法依赖于高维回归和高维协方差矩阵估计的新方法的集成，以及先前功能基因组和通路的新纳入。新方法可以应用于不同类型的基因组数据，理想地将有助于识别基因及其复杂的相互作用，以及各种复杂的人类疾病背后的生物途径。这里提出的工作将有助于建立高维基因组数据的模型和研究复杂的表型和生物系统的统计方法，并提供对各种数据集所代表的每个生物领域的见解。根据这笔赠款开发的所有项目和详细的文件将免费提供给感兴趣的研究人员。

DESCRIPTION (provided by applicant): The broad, long-term objective of this project concerns the development of novel statistical methods and computational tools for statistical and probabilistic modeling of large-scale multiple genomics data motivated by important biological questions and experiments. New high-throughput technologies and next generation sequencing are generating various types of very high-dimensional genomic and proteomic data and metadata (e.g., networks and pathways databases) in order to obtain a systems-level understanding of various complex phenotypes. As the amount and complexity of the data increases and as the questions being addressed become more sophisticated, statistical analysis methods that can integrate these genomic data and in the meanwhile can incorporate information about gene function and pathways into analysis of numerical vector/matrix data are required in order to draw valid statistical and biological inferences. The specific aims of the current project are to develop new statistical models and methods for integrative analysis of genomic data in the context of pathways and networks. Motivated by analysis of genetic genomics data and diverse cancer genomic data, the first aim is to develop novel statistical methods for estimating genotype-adjusted precision matrix for a set of genes at the transcriptional levels. The resulting regression coefficient matrix and sparse precision matrix provide important information on gene regulation when the cis- and trans-genetic effects on gene expressions are adjusted. The second aim is to develop high dimensional instrumental variable regression for eQTL data analysis in order the identify the potential causal genes for a phenotype where the genome-wide genotypes are served as instrumental variables. Aims 3 and 4 propose a set of new methods for gene set enrichment analysis, including methods for gene-set analysis by testing homogeneity of the covariance matrices and a class of multivariate random-set methods for integrative analysis of diverse genomic data. These methods hinge on novel integration of methods for high dimensional regression and high dimensional covariance matrix estimation and novel incorporation of prior functional gene sets and pathways. The new methods can be applied to different types of genomic data and will ideally help facilitate the identification of genes and their complex interactions as well as the biological pathways underlying various complex human diseases. The work proposed here will contribute statistical methodology to modeling high dimensional genomic data and to studying complex phenotypes and biological systems and offer insights into each of the biological areas represented by the various data sets. All programs developed under this grant and detailed documentation will be made available free-of-charge to interested researchers.