Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data

Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 differen...

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Bibliographic Details
Published in:Metabolites 2023-01, Vol.13 (1), p.97
Main Authors: Bongaerts, Michiel, Kulkarni, Purva, Zammit, Alan, Bonte, Ramon, Kluijtmans, Leo A J, Blom, Henk J, Engelke, Udo F H, Tax, David M J, Ruijter, George J G, Reinders, Marcel J T
Format: Article
Language:English
Subjects:
anomaly detection
Datasets
Decision making
Graph representations
IEM
Inborn errors of metabolism
Metabolites
Metabolomics
Methods
one-class methods
outlier detection
Performance evaluation
Support vector machines
untargeted metabolomics
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Summary:Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods and performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that , and also performed well. Furthermore, we demonstrated the importance of a PCA transform prior to applying an outlier detection method since we observed that this increases the performance of several outlier detection methods. For only one of the three metabolomics datasets, we observed clinically satisfying performances for some outlier detection methods, where we were able to detect 90% of the IEM patient samples while detecting no false positives. These results suggest that outlier detection methods have the potential to aid the clinical investigator in routine screening for IEM using untargeted metabolomics data, but also show that further improvements are needed to ensure clinically satisfying performances.
ISSN:2218-1989
2218-1989
DOI:10.3390/metabo13010097