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.
journal_name
Mol Biosystjournal_title
Molecular bioSystemsauthors
Thorne T,Fratta P,Hanna MG,Cortese A,Plagnol V,Fisher EM,Stumpf MPdoi
10.1039/c3mb25497fsubject
Has Abstractpub_date
2013-07-01 00:00:00pages
1736-42issue
7eissn
1742-206Xissn
1742-2051journal_volume
9pub_type
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