Session: Parallel session 3 - AI and Bioinformatics

A Complete End-to-End Workflow for untargeted LC-MS/MS Metabolomics Data Analysis in R

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.