TY - JOUR
T1 - Graphical modelling of molecular networks underlying sporadic inclusion body myositis
AU - Thorne, Thomas
AU - Fratta, Pietro
AU - Hanna, Michael G.
AU - Cortese, Andrea
AU - Plagnol, Vincent
AU - Fisher, Elizabeth M.
AU - Stumpf, Michael P H
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
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U2 - 10.1039/c3mb25497f
DO - 10.1039/c3mb25497f
M3 - Article
C2 - 23595110
AN - SCOPUS:84878737725
VL - 9
SP - 1736
EP - 1742
JO - Molecular BioSystems
JF - Molecular BioSystems
SN - 1742-206X
IS - 7
ER -