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 define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- Computational Research Division; Genomics Division; Life Sciences Division
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 948347
- Report Number(s):
- LBNL-382E; TRN: US200906%%317
- Journal Information:
- IEEE Transactions on Computational Biology and Bioinformatics, Journal Name: IEEE Transactions on Computational Biology and Bioinformatics
- Country of Publication:
- United States
- Language:
- English
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