Deciphering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers

Chadeau-Hyam, Marc, Campanella, Gianluca, Jombart, Thibaut, Bottolo, Leonardo, Portengen, Lutzen, Vineis, Paolo, Liquet, Benoit and Vermeulen, Roel C. H. (2013) Deciphering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers. Environmental And Molecular Mutagenesis, 54 7: 542-557. doi:10.1002/em.21797


Author Chadeau-Hyam, Marc
Campanella, Gianluca
Jombart, Thibaut
Bottolo, Leonardo
Portengen, Lutzen
Vineis, Paolo
Liquet, Benoit
Vermeulen, Roel C. H.
Title Deciphering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers
Journal name Environmental And Molecular Mutagenesis   Check publisher's open access policy
ISSN 0893-6692
1098-2280
Publication date 2013-01-01
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1002/em.21797
Volume 54
Issue 7
Start page 542
End page 557
Total pages 16
Place of publication Hoboken, NJ United States
Publisher John Wiley and Sons Inc
Language eng
Subject 2307 Health, Toxicology and Mutagenesis
2713 Epidemiology
2716 Genetics (clinical)
Abstract Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets. Environ. Mol. Mutagen. 54:542-557, 2013.
Keyword Biomarkers
OMICS data
Statistical review
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collection: School of Mathematics and Physics
 
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Created: Tue, 23 Sep 2014, 22:00:22 EST by Kay Mackie on behalf of School of Mathematics & Physics