Current breathomics—a review on data pre-processing techniques and machine learning in metabolomics breath analysis

Abstract

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.

Review on Ion Mobility Spectrometry. Part 2: hyphenated methods and effects of experimental parameters

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“…An extremely relevant part of supervised methods is the validation of the predicting algorithms, which can be either cross-validation (CV) within the existing dataset or ideally within a newly independently sampled dataset. A wide range of supervised methods for linear and nonlinear problems is available 123 . From those, the most used is the Partial Least-Square Discriminant Analysis (PLS-DA).…”

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“…For example, the collection, measurement, and interpretation of VOCs is in its early stages, and not easily applicable to the clinical setting (10). As commercial companies step forward to provide user-friendly clinical systems that simplify collection and analysis, we can expect that the complexity and cost will improve.…”