Object Oriented Data Analysis for Untargeted Metabolomics

PROJECT SUMMARY ABSTRACT In this Phase I SBIR applicationwe propose to develop and optimize functional Object Oriented Data AnalysisfOODAmethods and create a cloud based SaaS platform to analyze untargeted metabolomics dataUntargeted metabolomics quantifies the amount of known and unknown metabolites in samples with the purpose of finding known and unknown metabolites that correlate with subgroupse gdiseased or healthy tissueThe goal of this research is to identify biomarkers for diagnosis and treatment targetsHoweverthe biostatistical tools being used have not been optimized for this dataand so analyses lag behind the technical developmentFunctional OODA is an emerging area of statistics for analyzing functional datasuch as untargeted metabolomic retention time x m z intensity data metabolic dataThe goal of this project is to optimize fOODA for this data to provide more powerful statistical methods for investigatorsThis should help them identify metabolite biomarkers more efficiently and avoid incorrectly calling unimportant metabolites as clinically relevantIncorrect or inferior analyses results in loss of money and time running downstream experiments to validate these false positive callsThe innovation of this proposal is three folddevelopment of statistical models and methods specifically designed and optimized for rawnot pre processeduntargeted metabolomics intensity datadevelopment of methods that allow inclusion of metadata and other omics data to measure how these factors impact metabolomic profilesandimplementation of the methods in a cloud based SaaS solutionThe impact on metabolomics will be new tools for rigorous experimental designhypothesis testingand biostatistical analysis for discovery of disease biomarkersand to move preclinical discoveries from the lab to the clinic fasterBioRankings has filed patent protection for this technology PROJECT NARRATIVE Untargeted metabolomics is a new technology designed to discover `biomarkersandaposfor clinical diagnosis and as targets for new treatments in medicine and agricultureThis project proposes to create new statistical methods and cloud based SaaS platform designed specifically for the unique structure of this dataIf successfulthese tools will help researchers move untargeted metabolomics research from the lab to the clinic and farm