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

BMC Bioinformatics 11:585 (2010)
Publishers Version DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
as soon as is submitted to ZB.
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. 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. 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.
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Publication type Article: Journal article
Document type Scientific Article
Reviewing status