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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2007-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.0030206&type=printable |
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| _version_ | 1850182867207847936 |
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| 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 |
| collection | DOAJ |
| 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 |
| id | doaj-art-a48133e434464c6c8d0abe7582a9a309 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2007-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-a48133e434464c6c8d0abe7582a9a3092025-08-20T02:17:29ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582007-10-013102032204210.1371/journal.pcbi.0030206Construction, visualisation, and clustering of transcription networks from microarray expression data.Tom C FreemanLeon GoldovskyMarkus BroschStijn van DongenPierre MazièreRussell J GrocockShiri FreilichJanet ThorntonAnton J EnrightNetwork 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).https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.0030206&type=printable |
| spellingShingle | 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. PLoS Computational Biology |
| title | 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_short | Construction, visualisation, and clustering of transcription networks from microarray expression data. |
| title_sort | construction visualisation and clustering of transcription networks from microarray expression data |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.0030206&type=printable |
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