Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation.
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.
Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de
Andreas
A
Kowarsch
Florian
F
Blöchl
Sebastian
S
Bohl
Maria
M
Saile
Norbert
N
Gretz
Ursula
U
Klingmüller
Fabian J
FJ
Theis
D000465:Algorithms; D000818:Animals; D020869:Gene Expression Profiling; D015850:Interleukin-6 / Q000494:pharmacology*; D051188:Knowledge Bases; D051379:Mice; D040081:Protein Array Analysis / Q000379:methods; D015398:Signal Transduction
D015850:Interleukin-6