10.5281/zenodo.2432975
https://zenodo.org/records/2432975
oai:zenodo.org:2432975
Park, Younja
Younja
Park
Korea University
Roede, James R.
James R.
Roede
University of Colorado
Kong, Taewoon
Taewoon
Kong
Georgia Institute of Technology
Jones, Dean P
Dean P
Jones
Emory University
Lee, Kichun
Kichun
Lee
Hanyang University
A biplot correlation range for group-wise metabolite selection in mass spectrometry
Zenodo
2018
2018-12-19
eng
10.5281/zenodo.2432974
Creative Commons Attribution 4.0 International
Background
Analytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization. Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes.
Methods
We present a multivariate statistical approach termed ‘biplot correlation range (BCR)’ to provide the group-wise selection of metabolic markers contributing to metabolic phenotypes.
Results
Using a simulated multiple-layer system that often arises in complex biologic systems, we show the feasibility and superiority of the proposed approach in comparison of existing approaches based on false discovery rate and correlation. To demonstrate the proposed method in a real-life dataset, we used LC-MS based metabolomics to determine spectrum of metabolites present in liver mitochondria from wild-type (WT) mice and thioredoxin-2 transgenic (TG) mice. We select discriminatory variables in terms of increased score in the direction of class identity using BCR. The results show that BCR provides means to identify metabolites contributing to class separation in a manner that can complement a statistical method by false discovery rate in complex data analysis for predictive health and personalized medicine.