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Bioinformatics Advance Access originally published online on August 11, 2009
Bioinformatics 2009 25(19):2588-2594; doi:10.1093/bioinformatics/btp439
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Testing the hypothesis of tissue selectivity: the intersection–union test and a Bayesian approach

K. Van Deun 1,*, H. Hoijtink 2, L. Thorrez 3, L. Van Lommel 3, F. Schuit 3 and I. Van Mechelen 1

1Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium, 2Department of Methodology and Statistics, University of Utrecht, 3508 TC Utrecht, The Netherlands and 3Gene Expression Unit, Katholieke Universiteit Leuven, 3000 Leuven, Belgium

*To whom correspondence should be addressed.


  Abstract

Motivation: Finding genes that are preferentially expressed in a particular tissue or condition is a problem that cannot be solved by standard statistical testing procedures. A relatively unknown procedure that can be used is the intersection–union test (IUT). However, two disadvantages of the IUT are that it is conservative and it conveys only the information of the least differing target tissue–other tissue pair.

Results: We propose a Bayesian procedure that quantifies how much evidence there is in the overall expression profile for selective over-expression. In a small simulation study, it is shown that the proposed method outperforms the IUT when it comes to finding selectively expressed genes. An application to publicly available data consisting of 22 tissues shows that the Bayesian method indeed selects genes with functions that reflect the specific tissue functions. The proposed method can also be used to find genes that are underexpressed in a particular tissue.

Availability: Both MATLAB and R code that implement the IUT and the Bayesian procedure in an efficient way, can be downloaded at http://ppw.kuleuven.be/okp/software/BayesianIUT/.

Contact: katrijn.vandeun{at}psy.kuleuven.be

Associate Editor: Trey Ideker

Received on April 7, 2009; revised on July 8, 2009; accepted on July 13, 2009

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