Enhancing Statistical Power and Data Reliability in Lipidomics: A New Framework for Extraction Method Selection Using Coral as a Model System

Abstract

Background Lipids play essential roles in membrane structure, energy storage, and cellular signaling. Due to their metabolic complexity and chemical diversity, lipidomics studies require extraction methods tailored to specific biological questions. Traditional assessments of extraction efficiency focus on metrics such as feature count, intensity, or overall variability, but these do not necessarily reflect biological relevance. There is a critical need for a robust evaluation framework that enhances data quality while maintaining experimental efficiency. This study addresses the challenge of improving statistical power in lipidomics without increasing the number of biological replicates. Results Using coral samples as a model system, we evaluated multiple extraction techniques and demonstrated that optimized protocols improve both the number and intensity of detected features. More importantly, they significantly enhance statistical robustness by reducing within-group relative standard deviation (RSD) and increasing overall RSD—factors that directly impact statistical power. Incorporating extraction quality controls and technical replicates minimized batch effects and improved data reliability. Additionally, we found that slight differences in sample grinding procedures selectively affected specific lipid classes, potentially masking biologically meaningful signals. These findings underscore the importance of extraction method consistency and the implementation of quality controls throughout the lipidomics workflow.  Significance This study provides a new methodological framework for evaluating and optimizing lipid extraction techniques in metabolomics. By improving data quality and statistical power without increasing sample size, our approach enhances the reliability of biological interpretations and facilitates biomarker discovery — particularly in systems with high sample variability, such as coral lipidomics.

Keywords: lipidomics, statistical power in omics, batch effect, extraction method optimization, corals