Imperial College London
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Dr. Jia Li

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Reader in Biological Chemistry
 
 
 
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Contact

 

+44 (0)20 7594 3230jia.li

 
 
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Location

 

10.N2ACommonwealth BuildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Loo:2020:bioinformatics/btaa649,
author = {Loo, RL and Chan, Q and Antti, H and Li, JV and Ashrafian, H and Elliott, P and Stamler, J and Nicholson, JK and Holmes, E and Wist, J},
doi = {bioinformatics/btaa649},
journal = {Bioinformatics},
pages = {5229--5236},
title = {Manuscript Strategy for improved characterisation of human metabolic phenotypes using a COmbined Multiblock Principal components Analysis with Statistical Spectroscopy (COMPASS)},
url = {http://dx.doi.org/10.1093/bioinformatics/btaa649},
volume = {36},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Loo,RL
AU  - Chan,Q
AU  - Antti,H
AU  - Li,JV
AU  - Ashrafian,H
AU  - Elliott,P
AU  - Stamler,J
AU  - Nicholson,JK
AU  - Holmes,E
AU  - Wist,J
DO  - bioinformatics/btaa649
EP  - 5236
PY  - 2020///
SN  - 1367-4803
SP  - 5229
TI  - Manuscript Strategy for improved characterisation of human metabolic phenotypes using a COmbined Multiblock Principal components Analysis with Statistical Spectroscopy (COMPASS)
T2  - Bioinformatics
UR  - http://dx.doi.org/10.1093/bioinformatics/btaa649
UR  - https://www.ncbi.nlm.nih.gov/pubmed/32692809
UR  - https://academic.oup.com/bioinformatics/article/36/21/5229/5874439
UR  - http://hdl.handle.net/10044/1/82811
VL  - 36
ER  -
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