Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST
- Author(s)
- Kevin Mildau, Christoph Büschl, Jürgen Zanghellini, Justin J.J. Van Der Hooft
- Abstract
Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets that also represent an intuitive and scientifically meaningful first stage of analysis in untargeted metabolomics. Exploiting such feature sets, feature-set testing has emerged as an approach that is widely used in genomics and targeted metabolomics pathway enrichment analyses. It allows for formally combining groupings with statistical testing into more meaningful pathway enrichment conclusions. Here, we present msFeaST (mass spectral Feature Set Testing), a feature-set testing and visualization workflow for LC-MS/MS untargeted metabolomics data. Feature-set testing involves statistically assessing differential abundance patterns for groups of features across experimental conditions. We developed msFeaST to make use of spectral similarity-based feature groupings generated using k-medoids clustering, where the resulting clusters serve as a proxy for grouping structurally similar features with potential biosynthesis pathway relationships. Spectral clustering done in this way allows for feature group-wise statistical testing using the globaltest package, which provides high power to detect small concordant effects via joint modeling and reduced multiplicity adjustment penalties. Hence, msFeaST provides interactive integration of the semi-quantitative experimental information with mass-spectral structural similarity information, enhancing the prioritization of features and feature sets during exploratory data analysis.
- Organisation(s)
- Department of Analytical Chemistry
- External organisation(s)
- University of Natural Resources and Life Sciences, University of Johannesburg (UJ), Vienna Doctoral School in Chemistry (DoSChem), Wageningen University and Research Centre
- Journal
- Bioinformatics
- Volume
- 40
- ISSN
- 1367-4803
- DOI
- https://doi.org/10.1093/bioinformatics/btae584
- Publication date
- 10-2024
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 106005 Bioinformatics
- ASJC Scopus subject areas
- Statistics and Probability, Biochemistry, Molecular Biology, Computer Science Applications, Computational Theory and Mathematics, Computational Mathematics
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/dedcd760-fa36-445a-886e-da39e88dda56