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Open Access Research article

Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation

Andreas Kowarsch1*, Florian Blöchl1, Sebastian Bohl2, Maria Saile3, Norbert Gretz3, Ursula Klingmüller2 and Fabian J Theis14*

Author Affiliations

1 Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany

2 Division of Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany

3 Medical Research Center, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany

4 Department of Mathematical Science, Technische Universität München, Garching, Germany

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BMC Bioinformatics 2010, 11:585  doi:10.1186/1471-2105-11-585

Published: 30 November 2010

Abstract

Background

External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes is complicated, as they consist of multi-layered temporal responses. While classical analyses based on clustering or gene set enrichment only partly reveal this information, matrix factorization techniques are well suited for a detailed temporal analysis. In signal processing, factorization techniques incorporating data properties like spatial and temporal correlation structure have shown to be robust and computationally efficient. However, such correlation-based methods have so far not be applied in bioinformatics, because large scale biological data rarely imply a natural order that allows the definition of a delayed correlation function.

Results

We therefore develop the concept of graph-decorrelation. We encode prior knowledge like transcriptional regulation, protein interactions or metabolic pathways in a weighted directed graph. By linking features along this underlying graph, we introduce a partial ordering of the features (e.g. genes) and are thus able to define a graph-delayed correlation function. Using this framework as constraint to the matrix factorization task allows us to set up the fast and robust graph-decorrelation algorithm (GraDe). To analyze alterations in the gene response in IL-6 stimulated primary mouse hepatocytes, we performed a time-course microarray experiment and applied GraDe. In contrast to standard techniques, the extracted time-resolved gene expression profiles showed that IL-6 activates genes involved in cell cycle progression and cell division. Genes linked to metabolic and apoptotic processes are down-regulated indicating that IL-6 mediated priming renders hepatocytes more responsive towards cell proliferation and reduces expenditures for the energy metabolism.

Conclusions

GraDe provides a novel framework for the decomposition of large-scale 'omics' data. We were able to show that including prior knowledge into the separation task leads to a much more structured and detailed separation of the time-dependent responses upon IL-6 stimulation compared to standard methods. A Matlab implementation of the GraDe algorithm is freely available at http://cmb.helmholtz-muenchen.de/grade webcite.