Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage

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 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 DeepSVDD and R-graph performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that AE reconstruction error, Mahalanobis and PCA reconstruction error 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.

Multivariate equivalence testing for food safety assessment

Products for food and feed derived from genetically modified (GM) crops are only allowed on the market when they are deemed to be safe for human health and the environment. The European Food Safety Authority (EFSA) performs safety assessment including a comparative approach: the compositional characteristics of a GM genotype are compared to those of reference genotypes that have a history of safe use. Statistical equivalence tests are used to carry out such a comparative assessment. These tests are univariate and therefore only consider one measured variable at a time. Phenotypic data, however, often comprise measurements on multiple variables that must be integrated to arrive at a single decision on acceptance in the regulatory process. The surge of modern molecular phenotyping platforms further challenges this integration, due to the large number of characteristics measured on the plants. This paper presents a new multivariate equivalence test that naturally extends a recently proposed univariate equivalence test and allows to assess equivalence across all variables simultaneously. The proposed test is illustrated on plant compositional data from a field study on maize grain and on untargeted metabolomic data of potato tubers, while its performance is assessed on simulated data.