A Comprehensive Guide to Volatolomics Data Analysis.

Abstract

Volatolomics (or volatilomics), the study of volatile organic compounds, has emerged as a crucial field of metabolomics due
to its potential for non-invasive diagnostics and disease monitoring. However, analyzing high-resolution data generated by
mass spectrometry-based instrumentation remains challenging. This comprehensive guide provides an in-depth exploration
of volatolomics data analysis, highlighting the importance of subsequent steps, including data cleaning, pretreatment, and
statistical and machine learning techniques (dimensionality reduction, clustering, classification, and variable selection). The
choice of these methods, and the integration of data handling practices, such as missing data imputation, outlier detection,
model validation, and data integration, significantly impact the identification of meaningful metabolites and the accuracy of
diagnostic conclusions. This guide aims to familiarize the reader with the implications of various data analysis techniques in
volatolomics and their suitability for different applications. It emphasizes the necessity of understanding the strengths and
limitations of each method to make informed decisions that enhance the reliability of findings. By outlining these methodologies,
the guide aims to equip researchers with the knowledge needed to navigate the complexities of volatolomics data analysis. The
careful consideration of experimental design, data collection, and processing strategies is essential for the identification of
biomarkers, ultimately advancing the field and improving the understanding of metabolic processes in health and disease.