Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

— The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively de...

• Type: Journal
• Year: 2010
• Where: TCBB
• Authors: Oliver Rübel, Gunther H. Weber, Min-Yu Huang, E. Wes Bethel, Mark D. Biggin, Charless C. Fowlkes, Cris L. Luengo Hendriks, Soile V. E. Keränen, Michael B. Eisen, David W. Knowles, Jitendra Malik, Hans Hagen, Bernd Hamann
• Keywords: Gene Expression, Gene Expression Data, Gene Expression Patterns, TCBB 2010