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Activity Number: 391
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Korean International Statistical Society
Abstract #311145 View Presentation
Title: Hierarchical Peak Detection Algorithms for Comprehensive Two-Dimensional Gas Chromatography Time-Of-Flight Mass Spectrometry Data
Author(s): Seongho Kim*+ and Xiang Zhang and Changyu Shen
Companies: Wayne State University/Karmanos Cancer Institute and University of Louisville and Indiana University
Keywords: Hierarchical models ; Peak detection ; Mass spectrometry ; Bayes factors ; Exponentially modified Gaussian ; Mixture models
Abstract:

A novel hierarchical peak detection algorithm is developed for comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm simultaneously performs baseline correction and de-noising under NEB models and then detects peaks using a mixture of probability distribution to deal with the co-eluting peaks. After that, peak merging is rendered using the mass spectral similarities. The new algorithm was further compared with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used exponentially modified Gaussian (EMG) mixture models.


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