Selection and Integration of -Omics Data for Biomarkers Discovery

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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.