Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, qua...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Dirk Repsilber, Sabine Kern, Anna Telaar, Gerhard Walzl, Gillian F. Black, Joachim Selbig, Shreemanta K. Parida, Stefan H. E. Kaufmann, Marc Jacobsen
• Keywords: BMCBI 2010, Cell Types, Gene Expression, Heterogeneous Tissues