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

@article{citeulike:1826805, 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.}, author = {Freeman, Tom C. and Goldovsky, Leon and Brosch, Markus and van Dongen, Stijn and Mazi\&\#232;re, Pierre and Grocock, Russell J. and Freilich, Shiri and Thornton, Janet and Enright, Anton J. }, citeulike-article-id = {1826805}, doi = {10.1371/journal.pcbi.0030206}, journal = {PLoS Computational Biology}, keywords = {grn, network\_inference}, month = {October}, number = {10}, pages = {e206+}, posted-at = {2007-12-12 13:39:21}, priority = {4}, title = {Construction, Visualisation, and Clustering of Transcription Networks from Microarray Expression Data}, url = {http://dx.doi.org/10.1371/journal.pcbi.0030206}, volume = {3}, year = {2007} }

TY - JOUR ID - citeulike:1826805 L3 - citeulike-article-id:1826805 TI - Construction, Visualisation, and Clustering of Transcription Networks from Microarray Expression Data JF - PLoS Computational Biology VL - 3 IS - 10 SP - e206 N2 - 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. KW - grn KW - network_inference AU - Freeman, Tom AU - Goldovsky, Leon AU - Brosch, Markus AU - van Dongen, Stijn AU - Mazière, Pierre AU - Grocock, Russell AU - Freilich, Shiri AU - Thornton, Janet AU - Enright, Anton PY - 2007/10/01/ UR - http://dx.doi.org/10.1371/journal.pcbi.0030206 ER -