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Construction, Visualisation, and Clustering of Transcription Networks from Microarray Expression Data

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  • Tom C Freeman
  • Leon Goldovsky
  • Markus Brosch
  • Stijn van Dongen
  • Pierre Mazière
  • Russell J Grocock
  • Shiri Freilich
  • Janet Thornton
  • Anton J Enright

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 Express3D.: This paper describes a novel approach for analysis of gene expression data. In this approach, normalized gene expression data is transformed into a graph where nodes in the graph represent transcripts connected to each other by virtue of their coexpression across multiple tissues or samples. The graph paradigm has many advantages for such analyses. Graph clustering of the derived network performs extremely well in comparison to traditional pairwise schemes. We show that this approach is robust and able to accommodate large datasets such as the Genomics Institute of the Novartis Research Foundation mouse tissue atlas. The entire approach and algorithms are combined into a single open-source JAVA application that allows users to perform this analysis and further mining on their own data and to visualize the results interactively in 3-D. The approach is not limited to gene expression data but would also be useful for other complex biological datasets. We use the method to investigate the relationship between the phylogenetic age of transcripts and their tissue specificity.

Suggested Citation

  • Tom C Freeman & Leon Goldovsky & Markus Brosch & Stijn van Dongen & Pierre Mazière & Russell J Grocock & Shiri Freilich & Janet Thornton & Anton J Enright, 2007. "Construction, Visualisation, and Clustering of Transcription Networks from Microarray Expression Data," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-11, October.
  • Handle: RePEc:plo:pcbi00:0030206
    DOI: 10.1371/journal.pcbi.0030206
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    References listed on IDEAS

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    1. Hiroaki Kitano, 2002. "Computational systems biology," Nature, Nature, vol. 420(6912), pages 206-210, November.
    2. Paul Nurse, 2003. "Systems biology: Understanding cells," Nature, Nature, vol. 424(6951), pages 883-883, August.
    3. H. Jeong & S. P. Mason & A.-L. Barabási & Z. N. Oltvai, 2001. "Lethality and centrality in protein networks," Nature, Nature, vol. 411(6833), pages 41-42, May.
    4. Marvin Cassman, 2005. "Barriers to progress in systems biology," Nature, Nature, vol. 438(7071), pages 1079-1079, December.
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    Cited by:

    1. Gemma C Sharp & James L Hutchinson & Nanette Hibbert & Tom C Freeman & Philippa T K Saunders & Jane E Norman, 2016. "Transcription Analysis of the Myometrium of Labouring and Non-Labouring Women," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-21, May.
    2. Feher Kristen & Whelan James & Müller Samuel, 2011. "Assessing Modularity Using a Random Matrix Theory Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-34, September.

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