Abstract
Introduction:
Despite untargeted LC-MS/MS data being a powerful approach for large-scale metabolomics analysis, a significant challenge in the field lies in the reproducible and efficient analysis of such data, in particular. The power of R-based analysis workflows lies in their high customizability and adaptability to specific instrumental and experimental setups, but, while various specialized packages exist for individual analysis steps, their seamless integration and application to large cohort datasets remains elusive.
Methodology:
Addressing this gap, we present an comprehensible end-to-end R workflow that leverages xcms and packages of the RforMassSpectrometryenvironment to encompass all aspects of pre-processing and downstream analyses for LC-MS/MS datasets in a reproducible manner.
Results:
This poster/presentation delineates a step-by-step analysis of an example untargeted metabolomics dataset tailored to quantify the small polar metabolome in human plasma samples and aimed to identify differences between individuals suffering from a cardiovascular disease and healthy controls. The objective of the workflow is to meticulously detail each step, from the preprocessing of raw mzML files to the annotation of differentially abundant ions between the two groups.
Conclusion:
Our workflow seamlessly integrates Bioconductor packages, offering adaptability to diverse study designs and analysis requirements. This workflow facilitates preprocessing, feature detection, alignment, normalization, statistical analysis and annotation within a unified framework, thereby enhancing the efficiency of metabolomic investigations. We also discuss alternative approaches to accommodate various dataset and goals, while emphasizing proper quality management for LC-MS data analysis.