Unsupervised dimensionality reduction for exposome research.

Area of Science:

• Environmental Health Sciences
• Analytical Chemistry
• Biostatistics

Background:

• The exposome, encompassing environmental exposures, is crucial for understanding human health.
• High-resolution mass spectrometry (HRMS) advances exposome measurement, detecting low-abundance toxicants.
• HRMS data present high-dimensional challenges, with numerous correlated chemical features.

Purpose of the Study:

• To explore unsupervised dimension reduction techniques for analyzing complex exposome data.
• To evaluate the utility of Principal Component Analysis (PCA), Factor Analysis (FA), and Non-negative Matrix Factorization (NMF) in exposome research.
• To assess the strengths and limitations of these methods in linking exposome profiles to disease outcomes.

Main Methods:

• Application of Principal Component Analysis (PCA) for dimensionality reduction.
• Utilization of Factor Analysis (FA) to identify latent structures in exposome data.
• Implementation of Non-negative Matrix Factorization (NMF) for feature extraction.
• Comparative analysis of the interpretability and utility of each method.

Main Results:

• Dimension reduction techniques effectively capture variability in high-dimensional exposome datasets.
• PCA, FA, and NMF offer distinct approaches to simplifying complex chemical feature data.
• The choice of method impacts the interpretability of relationships between exposome and disease.

Conclusions:

• Unsupervised dimension reduction is vital for interpreting complex exposome data from HRMS.
• PCA, FA, and NMF provide valuable tools for exposome-disease association studies.
• Method selection should prioritize interpretability within specific research contexts.