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. 2010 Sep 1:4:121.
doi: 10.1186/1752-0509-4-121.

Gene set analysis exploiting the topology of a pathway

Affiliations

Gene set analysis exploiting the topology of a pathway

Maria Sofia Massa et al. BMC Syst Biol. .

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.

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Figures

Figure 1
Figure 1
BCR signaling pathway. B cell receptor signaling pathway in human taken from KEGG [9].
Figure 2
Figure 2
ERBB signaling pathway. ERBB signaling pathway in human taken from KEGG [9].
Figure 3
Figure 3
DAG D obtained from BCR pathway. DAG D corresponding to the BCR signaling pathway.
Figure 4
Figure 4
DAG, moral graph, triangulated graph. Example of a DAG (A), the corresponding moral graph (B), and one possible triangulated graph (C).
Figure 5
Figure 5
Moral graph Dm obtained from BCR pathway. Moral graph Dm corresponding to the BCR signaling pathway.
Figure 6
Figure 6
Triangulated graph Dt obtained from BCR pathway. Triangulated graph Dt corresponding to the BCR signaling pathway.
Figure 7
Figure 7
DAG D obtained from ERBB pathway. DAG D corresponding to the chosen portion of ERBB signaling pathway.
Figure 8
Figure 8
Moral graph Dm obtained from ERBB pathway. Moral graph Dm corresponding to the chosen portion of ERBB signaling pathway.
Figure 9
Figure 9
Triangulated graph Dt obtained from ERBB pathway. Triangulated graph Dt corresponding to the chosen portion of ERBB signaling pathway.

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