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, pro...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Andreas Kowarsch, Florian Blöchl, Sebastian Bohl, Maria Saile, Norbert Gretz, Ursula Klingmüller, Fabian J. Theis
• Keywords: BMCBI 2010, Business, Correlation Function, Factorization Techniques, Matrix Factorization