Global Point Matching Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Mass Spectrometry Data

• Activity Number: 175 - Clustering and Changepoint Analysis
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Korean International Statistical Society
• Author(s): Seongho Kim* and Zeyu Li and Xiang Zhang
• Companies: Wayne State University and Wayne State University and University of Louisville
• Keywords: MS Similarity; Point Matching Algorithm; Peak Alignment; GCxGC-MS; GC-MS; LC-MS

Abstract

The peak alignment is to align peaks derived by the same compounds in different biological samples and is a vital preprocessing step before downstream analysis for 2D-MS based mass spectrometry data. However, due to uncontrollable experimental conditions, a shift of retention times among samples inevitably occurs during 2D-MS experiments, making it difficult for aligning peaks. Many researches have developed various peak alignment algorithms to correct the retention time shifts for homogeneous, heterogeneous or both mass spectrometry data. Almost all existing algorithms have been focused on a local alignment and so are suffering from low accuracy especially when aligning dense biological data with many peaks. Thus, we developed four global peak alignment (GPA) algorithms using coherent point drift (CPD) point matching algorithms: retention time-based GPA, prior GPA, mixture GPA, and prior mixture GPA. These four GPA algorithms are applied to homogeneous and heterogeneous spiked-in compound standard and real biological GC×GC-MS data and compared with existing algorithms. The results show that our GPA algorithms perform better than all existing algorithms in terms of F1 score.