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  • Loo, Ruey LengCentre for Computational and Systems Medicine, WA, Perth, Australia; The Australian National Phenome Centre, Health Futures Institute, Murdoch University, WA, Perth, Australia (author)

Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS)

  • Article/chapterEnglish2020

Publisher, publication year, extent ...

  • 2020-07-21
  • Oxford University Press,2020
  • electronicrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:umu-183594
  • https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-183594URI
  • https://doi.org/10.1093/bioinformatics/btaa649DOI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • 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.

Subject headings and genre

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  • Chan, QueenieMRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom (author)
  • Antti, Henrik,1970-Umeå universitet,Kemiska institutionen(Swepub:umu)hean0004 (author)
  • Li, Jia VDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom (author)
  • Ashrafian, H.Department of Surgery and Cancer, Imperial College London, London, United Kingdom (author)
  • Elliott, PaulMRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom (author)
  • Stamler, JeremiahDepartment of Preventive Medicine, Feinberg School of Medicine, Northwestern University, IL, Chicago, United States (author)
  • Nicholson, Jeremy KCentre for Computational and Systems Medicine, WA, Perth, Australia; The Australian National Phenome Centre, Health Futures Institute, Murdoch University, WA, Perth, Australia (author)
  • Holmes, ElaineCentre for Computational and Systems Medicine, WA, Perth, Australia; The Australian National Phenome Centre, Health Futures Institute, Murdoch University, WA, Perth, Australia; Department of Surgery and Cancer, Imperial College London, London, United Kingdom (author)
  • Wist, JulienChemistry Department, Universidad Del Valle, Cali, Colombia (author)
  • Centre for Computational and Systems Medicine, WA, Perth, Australia; The Australian National Phenome Centre, Health Futures Institute, Murdoch University, WA, Perth, AustraliaMRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom (creator_code:org_t)

Related titles

  • In:Bioinformatics: Oxford University Press36:21, s. 5229-52361367-48031367-48111460-2059

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