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IEEE/ACM Trans Comput Biol Bioinform. 2010 Jan-Mar;7(1):64-79. doi: 10.1109/TCBB.2008.49.

Integrating data clustering and visualization for the analysis of 3D gene expression data.

Author information

1
Lawrence Berkeley National Laboratory, 1 CyclotronRoad, Berkeley, CA 94720, USA. ORuebel@lbl.gov

Abstract

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.

PMID:
20150669
DOI:
10.1109/TCBB.2008.49
[Indexed for MEDLINE]

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