Unlocking complex co-expression network using graphical models

许多慢性病是复杂的，并且非常异质。它们可以受到多种基因的影响， 结合生活方式和环境因素，一种疾病的患者可分为 子组，例如，阿尔茨海默病（AD）的癌症亚型或阶段。人们可以利用全基因组 基因表达数据来研究这些疾病的分子特征，这可能有助于了解 疾病的病因和指导精确治疗。图形模型是评估复杂的 大量基因之间的网络相互作用。发展生物统计学和机器学习 使用基因表达数据来估计这种有向图形模型的方法是本发明的主要目标。 项目为此，本项目包含以下研究活动：（1）鉴于已知的疾病亚型， Aim 1开发了新的技术来联合估计多个无向/有向图模型， 每个子类型的模型。(2)在目标2中，我们考虑了疾病亚型没有被定义为先验的情况。 我们提出通过基因表达聚类来识别疾病亚型，并且聚类的不确定性是 纳入目标1中的多个有向图模型的估计。(3)单细胞RNA测序 这项技术使研究人员能够分析同一患者的多个细胞。目标3侧重于 估计多个有向图模型（例如，对于肿瘤细胞的多个亚克隆，或多种类型的 脑细胞）。所提出的图形模型的有效性 估计方法将使用癌症和AD数据分析来证明。研究结果 这是一个巨大的潜力，可以为这些疾病的理解和精确治疗提供新的见解。 此外，这些方法具有足够的通用性，可以应用于分析其他疾病的组学数据， 好.研究小组将分发计算效率高和用户友好的软件包， 研究出版物、学术报告以及与癌症研究专家的合作， 神经系统疾病

Many chronic diseases are complex and very heterogeneous. They can be affected by multiple genes in combination with lifestyle and environmental factors, and patients of one disease can be divided into subgroups, e.g., cancer subtypes or stages of Alzheimer's disease (AD). One can use the genome-wide gene expression data to investigate these disease's molecular signatures, which may help understand disease etiology and guide precise treatments. Graphical models are powerful tools to estimate complex network interactions among a large number of genes. To develop biostatistical and machine learning methods to estimate such directed graphical models using gene expression data is the primary goal of this project. To this end, this project contains the following research activities: (1) Given known disease subtypes, Aim 1 develops novel techniques to jointly estimate multiple undirected/directed graphical models with one model per subtype. (2) In Aim 2, we consider the situation where disease subtypes are not defined a prior. We propose to identify disease subtypes by gene expression clustering, and the uncertainty of clustering is incorporated into the estimation of multiple directed graphical models in Aim 1. (3) Recent single cell RNAsequencing technology enables researchers to profile multiple cells from the same patient. Aim 3 focuses on estimating multiple directed graphical models (e.g., for multiple subclones of tumor cells, or multiple types of brain cells) using single cell RNA-seq data of one patient. The effectiveness of the proposed graphical model estimation methods will be demonstrated using cancer and AD data analysis. The research results have great potential to offer new insights on the understanding and precise treatments of these diseases. Furthermore, these methods are general enough to be applied to analyze omic data of other diseases as well. The research team will disseminate computational efficient and user-friendly software packages, research publications, academic presentations and collaborations with experts in cancer research and neurological diseases.