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

BMC Systems Biology

Rainer König³  Gerhard Reinelt²  Roland Eils⁴  Marcus Oswald³  Nico Rebel²  Gunnar Schramm⁴  Stefan Wiesberg²  Rosario M Piro¹ 

[1] Present address: German Consortium for Translational Cancer Research (DKTK) and Division of Molecular Genetics, German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ), Heidelberg, Germany;Institute of Computer Science and Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany;Hans-Knöll-Institute (HKI), Jena, Germany;Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, BioQuant, University of Heidelberg, Heidelberg, Germany

关键词: Gene expression;    Pathway networks;    Network topology;    Pathway analysis;   

Others  :  866385
DOI  :  10.1186/1752-0509-8-56

 received in 2014-01-14, accepted in 2014-04-29,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Piro et al.; licensee BioMed Central Ltd.

【 图 表 】

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