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Network topology-based detection of differential gene regulation and regulatory switches in cell metabolism and signaling

Rosario M Piro126, Stefan Wiesberg3, Gunnar Schramm12, Nico Rebel3, Marcus Oswald145, Roland Eils12, Gerhard Reinelt3 and Rainer König145*

Author Affiliations

1 Division of Theoretical Bioinformatics, German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ), Heidelberg, Germany

2 Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, BioQuant, University of Heidelberg, Heidelberg, Germany

3 Institute of Computer Science and Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany

4 Center for Sepsis Control and Care, University Hospital Jena, Jena, Germany

5 Hans-Knöll-Institute (HKI), Jena, Germany

6 Present address: German Consortium for Translational Cancer Research (DKTK) and Division of Molecular Genetics, German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ), Heidelberg, Germany

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BMC Systems Biology 2014, 8:56  doi:10.1186/1752-0509-8-56

Published: 16 May 2014

Abstract

Background

Common approaches to pathway analysis treat pathways merely as lists of genes disregarding their topological structures, that is, ignoring the genes' interactions on which a pathway's cellular function depends. In contrast, PathWave has been developed for the analysis of high-throughput gene expression data that explicitly takes the topology of networks into account to identify both global dysregulation of and localized (switch-like) regulatory shifts within metabolic and signaling pathways. For this purpose, it applies adjusted wavelet transforms on optimized 2D grid representations of curated pathway maps.

Results

Here, we present the new version of PathWave with several substantial improvements including a new method for optimally mapping pathway networks unto compact 2D lattice grids, a more flexible and user-friendly interface, and pre-arranged 2D grid representations. These pathway representations are assembled for several species now comprising H. sapiens, M. musculus, D. melanogaster, D. rerio, C. elegans, and E. coli. We show that PathWave is more sensitive than common approaches and apply it to RNA-seq expression data, identifying crucial metabolic pathways in lung adenocarcinoma, as well as microarray expression data, identifying pathways involved in longevity of Drosophila.

Conclusions

PathWave is a generic method for pathway analysis complementing established tools like GSEA, and the update comprises efficient new features. In contrast to the tested commonly applied approaches which do not take network topology into account, PathWave enables identifying pathways that are either known be involved in or very likely associated with such diverse conditions as human lung cancer or aging of D. melanogaster. The PathWave R package is freely available at http://www.ichip.de/software/pathwave.html webcite.

Keywords:
Pathway analysis; Network topology; Pathway networks; Gene expression