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

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 mi...

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Autores principales:	Tom C Freeman, Leon Goldovsky, Markus Brosch, Stijn van Dongen, Pierre Mazière, Russell J Grocock, Shiri Freilich, Janet Thornton, Anton J Enright
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2007
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a48133e434464c6c8d0abe7582a9a309
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spelling	oai:doaj.org-article:a48133e434464c6c8d0abe7582a9a3092021-11-25T05:41:01ZConstruction, visualisation, and clustering of transcription networks from microarray expression data.1553-734X1553-735810.1371/journal.pcbi.0030206https://doaj.org/article/a48133e434464c6c8d0abe7582a9a3092007-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.0030206https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Network 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).Tom C FreemanLeon GoldovskyMarkus BroschStijn van DongenPierre MazièreRussell J GrocockShiri FreilichJanet ThorntonAnton J EnrightPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 3, Iss 10, Pp 2032-2042 (2007)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Tom C Freeman Leon Goldovsky Markus Brosch Stijn van Dongen Pierre Mazière Russell J Grocock Shiri Freilich Janet Thornton Anton J Enright Construction, visualisation, and clustering of transcription networks from microarray expression data.
description	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).
format	article
author	Tom C Freeman Leon Goldovsky Markus Brosch Stijn van Dongen Pierre Mazière Russell J Grocock Shiri Freilich Janet Thornton Anton J Enright
author_facet	Tom C Freeman Leon Goldovsky Markus Brosch Stijn van Dongen Pierre Mazière Russell J Grocock Shiri Freilich Janet Thornton Anton J Enright
author_sort	Tom C Freeman
title	Construction, visualisation, and clustering of transcription networks from microarray expression data.
title_short	Construction, visualisation, and clustering of transcription networks from microarray expression data.
title_full	Construction, visualisation, and clustering of transcription networks from microarray expression data.
title_fullStr	Construction, visualisation, and clustering of transcription networks from microarray expression data.
title_full_unstemmed	Construction, visualisation, and clustering of transcription networks from microarray expression data.
title_sort	construction, visualisation, and clustering of transcription networks from microarray expression data.
publisher	Public Library of Science (PLoS)
publishDate	2007
url	https://doaj.org/article/a48133e434464c6c8d0abe7582a9a309
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Construction, visualisation, and clustering of transcription networks from microarray expression data.

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