Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block principal components analysis with statistical spectroscopy (COMPASS)

R.L. Loo; Q. Chan; H. Antti; J.V. Li; H. Ashrafian; P. Elliott; J. Stamler; J.K. Nicholson; E. Holmes; J. Wist; J. Wren

doi:10.1093/bioinformatics/btaa649

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Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block principal components analysis with statistical spectroscopy (COMPASS)

Journal article

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Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block principal components analysis with statistical spectroscopy (COMPASS)

R.L. Loo, Q. Chan, H. Antti, J.V. Li, H. Ashrafian, P. Elliott, J. Stamler, J.K. Nicholson, E. Holmes, J. Wist, …

Bioinformatics, Vol.36(21), pp.5229-5236

2020

DOI: https://doi.org/10.1093/bioinformatics/btaa649

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Abstract

Motivation Large-scale population omics data can provide insight into associations between gene–environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets. Results Here, we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is subsequently achieved by implementing Statistical TOtal Correlation SpectroscopY on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset are used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS method thus provides an efficient semi-automated approach for screening population datasets.

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Title: Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block principal components analysis with statistical spectroscopy (COMPASS)
Authors/Creators: R.L. Loo (Author/Creator) - Murdoch University
Q. Chan (Author/Creator) - Imperial College London
H. Antti (Author/Creator) - Umeå University
J.V. Li (Author/Creator) - Imperial College London
H. Ashrafian (Author/Creator) - Imperial College London
P. Elliott (Author/Creator) - Imperial College London
J. Stamler (Author/Creator) - Northwestern University
J.K. Nicholson (Author/Creator) - Murdoch University
E. Holmes (Author/Creator) - Imperial College London
J. Wist (Author/Creator) - Universidad del Valle
J. Wren (Author/Creator)
Publication Details: Bioinformatics, Vol.36(21), pp.5229-5236
Publisher: Oxford University Press
Identifiers: 991005540007807891
Copyright: © 2020 The Authors.
Murdoch Affiliation: Australian National Phenome Centre; Health Futures Institute
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 2 Chemistry; 2.211 Mass Spectrometry; 2.211.990 Metabolomics
Web Of Science research areas: Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability
ESI research areas: Biology & Biochemistry