Statistical Model Building for High Dimensional Biomedical Data

描述（申请人提供）：当前大规模生物医学数据的典型特征是观察样本数量少，观察样本异质性广泛。鉴定与样品表型相关的差异表达基因（例如，癌症疾病发展）和基于基因表达预测样品表型是微阵列数据分析中的一些中心研究问题。大多数现有的统计方法忽略了样本的异质性，从而失去了功效。 该项目提出开发新的统计方法，明确解决小样本量和样本异质性问题，并且可以非常普遍地应用。这些方法的有用性将显示与大规模的生物医学数据来自肺和肾移植研究项目。这些移植项目旨在通过鉴定分子生物标志物来预测同种异体移植排斥反应，以改善肺/肾移植排斥反应的分子诊断和治疗，从而进行关键的早期治疗和快速、非侵入性和经济的检测。 具体目标是：1）开发用于差异基因表达检测的新的统计方法，其明确地建模样品异质性。2)开发新的统计方法，用于对高维生物医学数据进行分类并纳入样本异质性。3)开发新的统计方法来联合分析一组基因（例如，基因在一条通路中）。4)使用开发的模型和方法来回答与肺和肾移植项目中的公共卫生相关的研究问题;并在用户友好和记录良好的软件中实施和验证拟议的方法，并免费将其分发给科学界。 寻找新的肺、肾移植排斥反应生物标志物具有重要意义。在容易获得的体液中快速可靠地检测和预测排斥反应可能会使临床干预试验快速发展。我们建议研究分析大规模生物医学数据的新方法，以充分发挥其分子诊断和移植排斥预测预后的潜力，以进行关键的早期治疗。

DESCRIPTION (provided by applicant): Typical of current large-scale biomedical data is the feature of small number of observed samples and the widely observed sample heterogeneity. Identifying differentially expressed genes related to the sample phenotye (e.g., cancer disease development) and predicting sample phenotype based on the gene expressions are some central research questions in the microarray data analysis. Most existing statistical methods have ignored sample heterogeneity and thus loss power. This project proposes to develop novel statistical methods that explicitly address the small sample size and sampe heterogeneity issues, and can be applied very generally. The usefulness of these methods will be shown with the large-scale biomedical data originating from the lung and kidney transplant research projects. The transplant projects aimed to improve the molecular diagnosis and therapy of lung/kidney allograft rejection by identifying molecular biomarkers to predict the allograft rejection for critical early treatment and rapid, noninvasive, and economical testing. The specific aims are 1) Develop novel statistical methods for differential gene expression detection that explicitly model sample heterogeneity. 2) Develop novel statistical methods for classifying high-dimensional biomedical data and incorporating sample heterogeneity. 3) Develop novel statistical methods for jointly analyzing a set of genes (e.g., genes in a pathway). 4) Use the developed models and methods to answer research questions relevant to public health in the lung and kidney transplant projects; and implement and validate the proposed methods in user-friendly and well-documented software, and distribute them to the scientific community at no charge. It is very important to identify new biomarkers of allograft rejection in lung and kidney transplant recipients. The rapid and reliable detection and prediction of rejection in easily obtainable body fluids may allow the rapid advancement of clinical interventional trials. We propose to study novel methods for analyzing the large-scale biomedical data to realize their full potential of molecular diagnosis and prognosis of transplant rejection prediction for critical early treatment.