Construction, visualisation, and clustering of transcription networks from microarray expression data.

Authors: Freeman TC; Goldovsky L; Brosch M; van Dongen S; Mazière P; Grocock RJ; Freilich S; Thornton J; Enright AJ

Abstract: Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express(3D).

Keywords: Algorithms; Animals; Cluster Analysis; Computational Biology/*methods; Gene Expression; Gene Expression Profiling/*methods; *Gene Expression Regulation; Gene Regulatory Networks; Imaging, Three-Dimensional; Mice; Oligonucleotide Array Sequence Analysis/*methods; Pattern Recognition, Automated; Software; *Transcription, Genetic
Journal: PLoS computational biology
Volume: 3
Issue: 10
Pages: 2032-42
Date: Oct. 31, 2007
PMID: 17967053
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Citation:

Freeman TC, Goldovsky L, Brosch M, van Dongen S, Mazière P, Grocock RJ, Freilich S, Thornton J, Enright AJ (2007) Construction, visualisation, and clustering of transcription networks from microarray expression data. PLoS computational biology 3: 2032-42.



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