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

Ruey Leng Loo | Zendy; Queenie Chan | Zendy; Henrik Antti | Zendy; Jia V. Li | Zendy; Hutan Ashrafian | Zendy; Paul Elliott | Zendy; Jeremiah Stamler | Zendy; Jeremy K. Nicholson | Zendy; Elaine Holmes | Zendy; Julien Wist | Zendy

Open Access

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

Author(s) -

Ruey Leng Loo,

Queenie Chan,

Henrik Antti,

Jia V. Li,

Hutan Ashrafian,

Paul Elliott,

Jeremiah Stamler,

Jeremy K. Nicholson,

Elaine Holmes,

Julien Wist

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa649

Subject(s) - principal component analysis , compass , computer science , population , data mining , source code , block (permutation group theory) , software , correlation , artificial intelligence , feature (linguistics) , pattern recognition (psychology) , mathematics , cartography , geography , linguistics , philosophy , demography , geometry , sociology , programming language , operating system

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.

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