Manuscript Strategy for improved characterisation of human metabolic phenotypes using a COmbined Multiblock Principal components Analysis with Statistical Spectroscopy (COMPASS)
File(s)
Published version
Author(s)
Type
Journal Article
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 Multiblock 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 achieved by implementing statistical correlation spectroscopy (STOCSY) on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset is used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS thus provides an efficient semi-automated approach for screening population datasets. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/cheminfo/COMPASS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Date Issued
2020-11-01
Date Acceptance
2020-07-15
Citation
Bioinformatics, 2020, 36 (21), pp.5229-5236
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Start Page
5229
End Page
5236
Journal / Book Title
Bioinformatics
Volume
36
Issue
21
Copyright Statement
© The Author(s) 2020. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/32692809
PII: 5874439
Subjects
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Bioinformatics
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2020-07-21