Selection and Integration of -Omics Data for Biomarkers Discovery

各种组学技术的最新进展允许同时定量数百至数千个分子（例如，基因），彻底改变了科学家寻找分子生物标志物来测量致病过程或对治疗的反应的方式。尽管公认的数量和质量的技术资源可用于生物标志物的研究，新的统计和计算方法，需要询问和理解这些技术产生的丰富信息。 我的研究计划提出了创新的统计方法来选择和整合相关的分子变量，其中经典的方法可能无法检测到它们与感兴趣的表型的关联。特别是，我将结合联合收割机的元素，从工具变量估计，鲁棒性和惩罚估计，以解释测量误差，混杂因素，离群观察和复杂的数据结构，这是常见的组学数据集，并可能危及临床有用的生物标志物的发现。 工具变量估计类似于经典的回归估计，但它们从补充变量（工具）中借用力量来解释测量误差和混杂因素。例如，遗传或基因组数据可以用作蛋白质组生物标志物发现研究中的工具，以评估蛋白质和临床表型之间的因果关系。由于组学研究需要分析大量的候选解释变量（例如，蛋白质）和潜在的工具（例如，基因），其中只有少数是相关的（稀疏模型），经典的IV估计不能使用。此外，组学数据集通常包含与技术问题或具有罕见分子谱的患者相关的外围观察结果。因此，开发对离群值和数据中的杠杆点具有鲁棒性的估计量具有根本的重要性。在文献中已经提出了惩罚回归估计器来估计从复杂数据集中选择最重要的解释变量的稀疏模型（例如，LASSO）。尽管在我过去的工作和文献中惩罚IV估计和稳健惩罚估计的一些初步结果，没有一个建议的估计整合所有三个组成部分：惩罚，鲁棒性和工具变量。有一个统一的框架，融合这些概念是必不可少的，以促进蛋白质组生物标志物的发现，通过利用合理的生物机制，相关的基因，蛋白质和疾病状态。虽然我的大部分研究都集中在统计蛋白质组学上，但所提出的分析技术与数据科学中常见的复杂高维数据的分析相关，为更广泛的社区带来价值。

Recent advances in various -omics technologies allow the simultaneous quantitation of hundreds to thousands of molecules simultaneously (e.g., genes), revolutionizing the way that scientists search for molecular biomarkers to measure pathogenic processes or responses to therapies. Despite the recognized number and quality of the technical resources available for biomarker studies, new statistical and computational methods are needed to interrogate and understand the rich information generated by these technologies. My research program proposes innovative statistical methods to select and integrate relevant molecular variables, where classical approaches may fail to detect their association with the phenotype of interest. In particular, I will combine elements from instrumental variables estimation, robustness, and penalized estimation to account for measurement errors, confounding factors, outlying observations, and complex data structures, which are common in -omics dataset and can jeopardize the discovery of clinically useful biomarkers. Instrumental variables estimators are analogous to classical regression estimators but they borrow strength from supplemental variables (the instruments) to account for measurement errors and confounding factors. For example, genetic or genomic data can be used as instruments in a proteomic biomarkers discovery studies to assess causal effects between proteins and clinical phenotypes. Since -omics studies require the analysis of a large number of candidate explanatory variables (e.g., proteins) and potential instruments (e.g., genes), of which only a few would be relevant (sparse model), classical IV estimators cannot be used. Furthermore, -omics datasets usually contain outlying observations associated, for example, with technical problems or patients with rare molecular profiles. Thus, the development of estimators that are robust to outliers and leverage points in the data is of fundamental importance. Penalized regression estimators have been proposed in the literature to estimate sparse models selecting the most important explanatory variables from complex datasets (e.g., LASSO). Despite some initial results on penalized IV estimators and robust penalized estimators in my past work and the literature, none of the proposed estimators integrates all three components: penalization, robustness, and instrumental variables. Having a unifying framework that blends these concepts is essential to boost proteomic biomarker discoveries by exploiting the plausible biological mechanisms that relate genes, proteins, and disease state. Although most of my research is focused in Statistical Proteomics, the analytical technics proposed are relevant for the analysis of complex high-dimensional data commonly found in Data Science bringing value to a broader community.