title: Graphical modelling of molecular networks underlying sporadic inclusion body myositis.
abstract: Here we present a novel statistical methodology that allows us to analyze gene expression data that have been collected from a number of different cases or conditions in a unified framework. Using a Bayesian nonparametric framework we develop a hierarchical model wherein genes can maintain a shared set of interactions between different cases, whilst also exhibiting behaviour that is unique to specific cases, sets of conditions, or groups of data points. By doing so we are able to not only combine data from different cases but also to discern the unique regulatory interactions that differentiate the cases. We apply our method to clinical data collected from patients suffering from sporadic Inclusion Body Myositis (sIBM), as well as control samples, and demonstrate the ability of our method to infer regulatory interactions that are unique to the disease cases of interest. The method thus balances the statistical need to include as many patients and controls as possible, and the clinical need to maintain potentially cryptic differences among patients and between patients and controls at the regulatory level.
authors.affiliation: Centre for Bioinformatics and Systems Biology, Imperial College London, London, UK. thomas.thorne@imperial.ac.uk
authors.forename: Thomas
authors.initials: T
authors.lastname: Thorne
authors.forename: Pietro
authors.initials: P
authors.lastname: Fratta
authors.forename: Michael G
authors.initials: MG
authors.lastname: Hanna
authors.forename: Andrea
authors.initials: A
authors.lastname: Cortese
authors.forename: Vincent
authors.initials: V
authors.lastname: Plagnol
authors.forename: Elizabeth M
authors.initials: EM
authors.lastname: Fisher
authors.forename: Michael P H
authors.initials: MP
authors.lastname: Stumpf
mesh_terms: D000465:Algorithms; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological; D018979:Myositis, Inclusion Body / Q000235:genetics*