This project aims to improve the processing and interpretation of liquid chromatography hyphenated high-resolution mass spectrometry (LC-HRMS) data in untargeted metabolomics. These datasets contain thousands of molecules, together with isotopic and adduct patterns. Metabolomics studies comprise hundreds of such datasets, each resulting from an independent run. Current workflows typically process each run independently to generate peak lists before multivariate analysis. We propose an alternative strategy in which multiple runs are first fused into a common high-quality representation, enabling mathematical enhancement and extraction of the most relevant signals. The approach will be implemented in the Finnee2024 MATLAB toolbox and evaluated on publicly available datasets from open metabolomics repositories. The project will involve Master’s students in data curation, workflow execution, and scientific documentation, strengthening their skills in programming, data analysis, and reproducible research. The expected outcome is a more robust and interpretable LC-HRMS data-processing framework that can support future metabolomics studies and competitive grant applications.
This project aims to improve the processing and interpretation of liquid chromatography hyphenated high-resolution mass spectrometry (LC-HRMS) data in untargeted metabolomics. These datasets contain thousands of molecules, together with isotopic and adduct patterns. Metabolomics studies comprise hundreds of such datasets, each resulting from an independent run. Current workflows typically process each run independently to generate peak lists before multivariate analysis. We propose an alternative strategy in which multiple runs are first fused into a common high-quality representation, enabling mathematical enhancement and extraction of the most relevant signals. The approach will be implemented in the Finnee2024 MATLAB toolbox and evaluated on publicly available datasets from open metabolomics repositories. The project will involve Master’s students in data curation, workflow execution, and scientific documentation, strengthening their skills in programming, data analysis, and reproducible research. The expected outcome is a more robust and interpretable LC-HRMS data-processing framework that can support future metabolomics studies and competitive grant applications.
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