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ToxSci Advance Access originally published online on January 4, 2008
Toxicological Sciences 2008 102(2):444-454; doi:10.1093/toxsci/kfn001
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© The Author 2008. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Integration of Clinical Chemistry, Expression, and Metabolite Data Leads to Better Toxicological Class Separation

Jeppe S. Spicker*,{dagger},1, Søren Brunak*, Klaus S. Frederiksen{ddagger} and Henrik Toft{dagger}

* Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Kemitorvet, Building 208, DK-2800 Lyngby, Denmark {dagger} Protein Structure and Biophysics, Protein Engineering {ddagger} Molecular Genetics, Biotechnology, Novo Nordisk Park, DK-2760 Måløv, Denmark

1 To whom correspondence should be addressed. Fax: +45-45931585. E-mail: skytte{at}cbs.dtu.dk.

Received August 29, 2007; accepted December 21, 2007


  Abstract

A large number of databases are currently being implemented within toxicology aiming to integrate diverse biological data, such as clinical chemistry, expression, and other types of data. However, for these endeavors to be successful, tools for integration, visualization, and interpretation are needed. This paper presents a method for data integration using a hierarchical model based on either principal component analysis or partial least squares discriminant analysis of clinical chemistry, expression, and nuclear magnetic resonance data using a toxicological study as case. The study includes the three toxicants alpha-naphthyl-isothiocyanate, dimethylnitrosamine, and N-methylformamide administered to rats. Improved predictive ability of the different classes is seen, suggesting that this approach is a suitable method for data integration and visualization of biological data. Furthermore, the method allows for correlation of biological parameters between the different data types, which could lead to an improvement in biological interpretation.

Key Words: data integration; toxicology; clinical chemistry; microarray; metabonomics.


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