Big Data Analysis on Puerto Rico Testsite for Exploring Contamination Threats

Area of Science:

• Environmental Health Sciences
• Biomedical Data Science
• Toxicology

Background:

• The Puerto Rico Testsite for Exploring Contamination Threats (P-TECT) Center investigates links between environmental pollutants and adverse health outcomes, specifically preterm birth.
• Efficient analysis of biological samples is crucial for understanding these environmental health linkages.
• Existing spectral analysis methods can be time-consuming, hindering large-scale research efforts.

Purpose of the Study:

• To accelerate the spectral analysis of biological samples using advanced computational methods.
• To present a data repository developed for the P-TECT Center.
• To demonstrate a use case for analyzing biological sample data within the repository.

Main Methods:

• Implementation of Principal Component Analysis (PCA) for spectral data reduction and pattern recognition.
• Development of customized software to automate and expedite the spectral analysis workflow.
• Establishment of a comprehensive data repository for biological sample information.

Main Results:

• PCA and customized software significantly reduced the time required for spectral analysis of biological samples.
• The developed data repository effectively stores and manages diverse biological sample data.
• A use case analysis confirmed the utility of the system for investigating environmental health questions.

Conclusions:

• The combined approach of PCA and tailored software offers an efficient solution for high-throughput spectral analysis in environmental health research.
• The P-TECT data repository provides a valuable resource for studying the impact of environmental pollutants on preterm birth.
• This methodology enhances the capacity to establish critical linkages between environmental exposures and health outcomes.