EDISA: extracting biclusters from multiple time-series of gene expression profiles

Background: Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets. Results: In this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression module...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Jochen Supper, Martin Strauch, Dierk Wanke, Klaus Harter, Andreas Zell
• Keywords: BMCBI 2007, Gene Expression Modules, Gene-condition-time Datasets, Time-series Datasets