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Open Access Highly Accessed Methodology article

Gene set analysis exploiting the topology of a pathway

Maria Sofia Massa1, Monica Chiogna1* and Chiara Romualdi2

Author Affiliations

1 Department of Statistical Sciences, University of Padova, via C. Battisti 241, Padova, Italy

2 Department of Biology, University of Padova, via U. Bassi 58/B, Padova, Italy

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BMC Systems Biology 2010, 4:121  doi:10.1186/1752-0509-4-121

Published: 1 September 2010

Abstract

Background

Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it.

Results

We propose a novel gene set analysis specifically designed for gene sets defined by pathways. Such analysis, based on graphical models, explicitly incorporates the dependence structure among genes highlighted by the topology of pathways. The analysis is designed to be used for overall surveillance of changes in a pathway in different experimental conditions. In fact, under different circumstances, not only the expression of the genes in a pathway, but also the strength of their relations may change. The methods resulting from the proposal allow both to test for variations in the strength of the links, and to properly account for heteroschedasticity in the usual tests for differential expression.

Conclusions

The use of graphical models allows a deeper look at the components of the pathway that can be tested separately and compared marginally. In this way it is possible to test single components of the pathway and highlight only those involved in its deregulation.