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Issue No. 01 - January-March (2010 vol. 7)
ISSN: 1545-5963
pp: 1
H. Hagen , Univ. of Kaiserslautern, Kaiserslautern, Germany
J. Malik , Dept. of Electr. Eng. & Comput. Sci., Univ. of California, Berkeley, CA, USA
D.W. Knowles , Life Sci. Div., Lawrence Berkeley Nat. Lab., Berkeley, CA, USA
M.B. Eisen , Dept. of Mol. & Cell Biol., Univ. of California, Berkeley, Berkeley, CA, USA
S.V.E. Keranen , Genomics Div., Lawrence Berkeley Nat. Lab., Berkeley, CA, USA
C.L. Luengo Hendriks , Centre for Image Anal., Swedish Univ. of Agric. Sci., Uppsala, Sweden
C.C. Fowlkes , Donald Bren Sch. of Inf. & Comput. Sci., Univ. of California, Irvine, CA, USA
M.D. Biggin , Genomics Div., Lawrence Berkeley Nat. Lab., Berkeley, CA, USA
E.W. Bethel , Comput. Res. Div., Lawrence Berkeley Nat. Lab., Berkeley, CA, USA
B. Hamann , Dept. of Comput. Sci., Univ. of California, Davis, Davis, CA, USA
Min-Yu Huang , Dept. of Comput. Sci., Univ. of California, Davis, Davis, CA, USA
G.H. Weber , Dept. of Mol. & Cell Biol., Univ. of California, Berkeley, Berkeley, CA, USA
O. Rubel , Centre for Image Anal., Swedish Univ. of Agric. Sci., Uppsala, Sweden
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.
INDEX TERMS
pattern clustering, bioinformatics, classification, data visualisation, genetics, molecular biophysics, regulatory transcription factors, data clustering, data visualization, spatial gene expression patterns, three-dimensional image data, complex gene regulatory networks, data classification, mRNA patterns, Data visualization, Gene expression, Pattern analysis, Laboratories, Cyclotrons, Postal services, Data analysis, Bioinformatics, Data mining, Embryo, spatial expression pattern., Bioinformatics visualization, multimodal visualization, integrating Infovis/Scivis, visual data mining, three-dimensional gene expression, data clustering, cluster visualization, gene expression pattern, temporal expression variation, gene regulation, spatial expression pattern, bioinformatics visualization, multimodal visualization, integrating Infovis/Scivis, visual data mining, three dimensional gene expression, data clustering, cluster visualization, gene expression pattern, temporal expression variation, gene regulation
CITATION
H. Hagen, J. Malik, D.W. Knowles, M.B. Eisen, S.V.E. Keranen, C.L. Luengo Hendriks, C.C. Fowlkes, M.D. Biggin, E.W. Bethel, B. Hamann, Min-Yu Huang, G.H. Weber, O. Rubel, "Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. , pp. 1, January-March 2010, doi:10.1109/TCBB.2008.49
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