This article is part of the supplement: A Semantic Web for Bioinformatics: Goals, Tools, Systems, Applications
Reconstructing networks of pathways via significance analysis of their intersections
1 Centro Interdipartimentale “L. Galvani”, Università di Bologna, , Bologna 40127, Italy
2 Dipartimento di Morfofisiologia veterinaria e Produzioni Animali (DIMORFIPA), Università di Bologna, Bologna 40064, Italy
3 Department of Physics , Università di Bologna, Bologna 40127, Italy
4 Institute for Brain and Neural Systems, Brown University, Providence RI 02906, USA
5 Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02903, USA
6 Department of Physics, Brown University, Providence RI 02906, USA
7 Istituto di Tecnologie Biomediche (ITB) CNR, Milano 20090, Italy
BMC Bioinformatics 2008, 9(Suppl 4):S9 doi:10.1186/1471-2105-9-S4-S9Published: 25 April 2008
Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statistical test. This problem is typically approached by applying post hoc corrections to control the false discovery rate, without taking into account prior biological knowledge. Pathway or gene ontology analysis can provide an alternative way to relax the significance threshold applied to single genes and may lead to a better biological interpretation.
Here we propose a new analysis method based on the study of networks of pathways. These networks are reconstructed considering both the significance of single pathways (network nodes) and the intersection between them (links).
We apply this method for the reconstruction of networks of pathways to two gene expression datasets: the first one obtained from a c-Myc rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein; the second one obtained from the comparison of Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia derived from bone marrow samples.
Our method extends statistical models that have been recently adopted for the significance analysis of functional groups of genes to infer links between these groups. We show that groups of genes at the interface between different pathways can be considered as relevant even if the pathways they belong to are not significant by themselves.