title: Principal bicorrelation analysis: Unraveling associations between three data sources.
abstract: In this article, we propose a statistical explorative method for data integration. It is developed in the context of early drug development for which it enables the detection of chemical substructures and the identification of genes that mediate their association with the bioactivity (BA). The core of the method is a sparse singular value decomposition for the identification of the gene set and a permutation-based method for the control of the false discovery rate. The method is illustrated using a real dataset, and its properties are empirically evaluated by means of a simulation study. Quantitative Structure Transcriptional Activity Relationship (QSTAR, www.qstar-consortium.org ) is a new paradigm in early drug development that extends QSAR by not only considering data on the chemical structure of the compounds and on the compound-induced BA, but by simultaneously using transcriptomics data (gene expression). This approach enables, for example, the detection of chemical substructures that are associated with BA, while at the same time a gene set is correlated with both these substructures and the BA. Although causal associations cannot be formally concluded, these associations may suggest that the compounds act on the BA through a particular genomic pathway.
authors.affiliation: a Department of Mathematical Modelling , Statistics and Bioinformatics, Ghent University , Gent , Belgium.
authors.forename: Federico
authors.initials: F
authors.lastname: Mattiello
authors.affiliation: a Department of Mathematical Modelling , Statistics and Bioinformatics, Ghent University , Gent , Belgium.|b National Institute for Applied Statistics Research Australia (NIASRA) , School of Mathematics and Applied Statistics, University of Wollongong , Wollongong , Australia.
authors.forename: Olivier
authors.initials: O
authors.lastname: Thas
authors.affiliation: c Janssen Pharmaceutica , Beerse , Belgium.
authors.forename: Bie
authors.initials: B
authors.lastname: Verbist
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015195:Drug Design; D015870:Gene Expression; D020869:Gene Expression Profiling; D021281:Quantitative Structure-Activity Relationship
keywords: Data integration; multivariate analysis; sparse singular value decomposition