Integrating data clustering and visualization for the analysis of 3D gene expression data
2010 (English)In: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964, Vol. 7, no 1, 64-79 p.Article in journal (Refereed) Published
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 data sets. 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 1) the integration of data clustering and visualization into one framework, 2) the application of data clustering to 3D gene expression data, 3) the evaluation of the number of clusters k in the context of 3D gene expression clustering, and 4) the improvement of overall analysis quality via dedicated postprocessing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.
Place, publisher, year, edition, pages
2010. Vol. 7, no 1, 64-79 p.
Bioinformatics visualization, Cluster visualization, Data clustering, Gene expression pattern, Integrating Infovis/Scivis, Multimodal visualization, Spatial expression pattern, Three-dimensional gene expression, Visual data mining
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-121112DOI: 10.1109/TCBB.2008.49ISI: 000274063600006PubMedID: 20150669OAI: oai:DiVA.org:uu-121112DiVA: diva2:304561