A2031
Title: Conformalized outlier detection for mass spectrometry data
Authors: Johan Lim - Seoul National University (Korea, South)
Yangha Chung - Seoul National University (Korea, South) [presenting]
Xinlei Wang - Southern Methodist University (United States)
Soohyun Ahn - Ajou University (Korea, South)
Abstract: Quality control procedures are crucial for ensuring the reliability of mass spectrometry (MS) data, vital in biomarker discovery and understanding complex biological systems. However, existing methods often concentrate solely on either sample or peak outlier detection, rely on subjective criteria, and employ overly uniform thresholds based on asymptotic distributions, thereby failing to adequately capture the characteristics of the data. A novel approach, CPOD (Conformal Prediction for Outlier Detection), is introduced leveraging conformal prediction for outlier detection in MS data analysis. CPOD simultaneously identifies outlier samples and peaks based on data-driven and distribution-free principles. Rigorous numerical evaluations and comparisons with existing methods demonstrate superior diagnostic performance. Application to real LC-MRM data underscores practical utility, enhancing data reliability and reproducibility in MS studies.
