Analyzing M-CSF dependent monocyte/macrophage differentiation: Expression modes and meta-modes derived from an independent compo

Background: The analysis of high-throughput gene expression data sets derived from microarray experiments still is a field of extensive investigation. Although new approaches and algorithms are published continuously, mostly conventional methods like hierarchical clustering algorithms or variance analysis tools are used. Here we take a closer look at independent component analysis (ICA) which is already discussed widely as a new analysis approach. However, deep exploration of its applicability and relevance to concrete biological problems is still missing. In this study, we investigate the relevance of ICA in gaining new insights into well characterized regulatory mechanisms of M-CSF dependent macrophage differentiation. Results: Statistically independent gene expression modes (GEM) were extracted from observed gene expression signatures (GES) through ICA of different microarray experiments. From each GEM we deduced a group of genes, henceforth called sub-mode. These sub-modes were fu...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Dominik Lutter, Peter Ugocsai, Margot Grandl, Evelyn Orso, Fabian J. Theis, Elmar Wolfgang Lang, Gerd Schmitz
• Keywords: Analysis, BMCBI 2008, Gene Expression, Microarray Data