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Metabolites 2019, 9(3), 54; https://doi.org/10.3390/metabo9030054

Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis

1
Adrem Data Lab, Department of Mathematics and Computer Science, University of Antwerp, 2000 Antwerp, Belgium
2
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
3
Natural Products & Food Research and Analysis (NatuRA), Department of Pharmaceutical Sciences, University of Antwerp, 2000 Antwerp, Belgium
4
Toxicological Center, Department of Pharmaceutical Sciences, University of Antwerp, 2000 Antwerp, Belgium
*
Authors to whom correspondence should be addressed.
Received: 18 January 2019 / Revised: 5 March 2019 / Accepted: 18 March 2019 / Published: 20 March 2019
(This article belongs to the Special Issue Signal Processing and Machine Learning for Metabolomics)
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Abstract

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available. View Full-Text
Keywords: machine learning; dynamic metabolomics; data simulation machine learning; dynamic metabolomics; data simulation
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Beirnaert, C.; Peeters, L.; Meysman, P.; Bittremieux, W.; Foubert, K.; Custers, D.; Van der Auwera, A.; Cuykx, M.; Pieters, L.; Covaci, A.; Laukens, K. Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis. Metabolites 2019, 9, 54.

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