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Constrained principal component analysis with stochastically ordered scores for high-dimensional mass spectrometry data

Authors
 Hyeong Jin Hyun  ;  Youngrae Kim  ;  Sun Jo Kim  ;  Joungyeon Kim  ;  Johan Lim  ;  Dong Kyu Lim  ;  Sung Won Kwon 
Citation
 CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, Vol.216 : 104376, 2021-09 
Journal Title
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
ISSN
 0169-7439 
Issue Date
2021-09
Keywords
Biconvex problem ; Constrained principal component analysis ; Mass spectrometry ; Lipidomics data ; Stochastic order
Abstract
In this paper, we consider a constrained principal component analysis (PCA) for the projection of high-dimensional samples from different groups to a lower-dimensional space for which the principal scores are stochastically ordered over the groups. We express the problem as the minimization of a constrained biconvex problem and develop an iterative algorithm to solve it. We numerically show that the solution to our constrained PCA problem approximately rotates the principal coordinates of the ordinary PCA to achieve ordered scores. Consequently, our approach significantly improves the scores and the corresponding loading matrix compared to the original PCA if their true values are ordered over groups. We finally apply our method to two data examples: (i) the direct infusion multiple reaction monitoring mass spectrometry (DI-MRM-MS) data of white rice to verify the authenticity of adulterated Japonica rice and (ii) high-dimensional lipidomics data from ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS) analysis of patients with liver diseases.
Full Text
https://www.sciencedirect.com/science/article/pii/S0169743921001441
DOI
10.1016/j.chemolab.2021.104376
Appears in Collections:
3. College of Nursing (간호대학) > Dept. of Nursing (간호학과) > 1. Journal Papers
Yonsei Authors
Joungyoun Kim(김정연)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/190530
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