Non-linear mapping for exploratory data analysis in functional genomics
Identifieur interne : 000310 ( Pmc/Curation ); précédent : 000309; suivant : 000311Non-linear mapping for exploratory data analysis in functional genomics
Auteurs : Francisco Azuaje [Royaume-Uni] ; Haiying Wang [Royaume-Uni] ; Alban Chesneau [France]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2005.
Abstract
Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisation-driven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigated
Publicly available expression data originating from leukaemia, round blue-cell tumours and Parkinson disease studies were analysed. The method distinguished relevant clusters and critical analysis areas. The system does not require assumptions about the inherent class structure of the data, its mapping process is controlled by only one parameter and the resulting transformations offer intuitive, meaningful visual displays. Comparisons with traditional mapping models are presented. As a way of promoting potential, alternative applications of the methodology presented, an example of exploratory data analysis of interactome networks is illustrated. Data from the
A relaxation method for non-linear mapping provided the basis for visualisation-driven analyses using different types of data. This study indicates that such a system may represent a user-friendly and robust approach to exploratory data analysis. It may allow users to gain better insights into the underlying data structure, detect potential outliers and assess assumptions about the cluster composition of the data.
Url:
DOI: 10.1186/1471-2105-6-13
PubMed: 15661072
PubMed Central: 548129
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisation-driven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigated</p>
</sec>
<sec><title>Results</title>
<p>Publicly available expression data originating from leukaemia, round blue-cell tumours and Parkinson disease studies were analysed. The method distinguished relevant clusters and critical analysis areas. The system does not require assumptions about the inherent class structure of the data, its mapping process is controlled by only one parameter and the resulting transformations offer intuitive, meaningful visual displays. Comparisons with traditional mapping models are presented. As a way of promoting potential, alternative applications of the methodology presented, an example of exploratory data analysis of interactome networks is illustrated. Data from the <italic>C. elegans </italic>
interactome were analysed. Results suggest that this method might represent an effective solution for detecting key network hubs and for clustering biologically meaningful groups of proteins.</p>
</sec>
<sec><title>Conclusion</title>
<p>A relaxation method for non-linear mapping provided the basis for visualisation-driven analyses using different types of data. This study indicates that such a system may represent a user-friendly and robust approach to exploratory data analysis. It may allow users to gain better insights into the underlying data structure, detect potential outliers and assess assumptions about the cluster composition of the data.</p>
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<journal-title>BMC Bioinformatics</journal-title>
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<given-names>Francisco</given-names>
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<email>hy.wang@ulster.ac.uk</email>
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School of Computing and Mathematics, University of Ulster, BT37 0QB, UK</aff>
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Molecular Genetics Institute, CNRS UMR5535, 1919, route de Mende, 34293 Montpellier, France</aff>
<pub-date pub-type="collection"><year>2005</year>
</pub-date>
<pub-date pub-type="epub"><day>20</day>
<month>1</month>
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<volume>6</volume>
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<history><date date-type="received"><day>9</day>
<month>8</month>
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<copyright-statement>Copyright © 2005 Azuaje et al; licensee BioMed Central Ltd.</copyright-statement>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0"></ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>
</license>
<abstract><sec><title>Background</title>
<p>Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisation-driven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigated</p>
</sec>
<sec><title>Results</title>
<p>Publicly available expression data originating from leukaemia, round blue-cell tumours and Parkinson disease studies were analysed. The method distinguished relevant clusters and critical analysis areas. The system does not require assumptions about the inherent class structure of the data, its mapping process is controlled by only one parameter and the resulting transformations offer intuitive, meaningful visual displays. Comparisons with traditional mapping models are presented. As a way of promoting potential, alternative applications of the methodology presented, an example of exploratory data analysis of interactome networks is illustrated. Data from the <italic>C. elegans </italic>
interactome were analysed. Results suggest that this method might represent an effective solution for detecting key network hubs and for clustering biologically meaningful groups of proteins.</p>
</sec>
<sec><title>Conclusion</title>
<p>A relaxation method for non-linear mapping provided the basis for visualisation-driven analyses using different types of data. This study indicates that such a system may represent a user-friendly and robust approach to exploratory data analysis. It may allow users to gain better insights into the underlying data structure, detect potential outliers and assess assumptions about the cluster composition of the data.</p>
</sec>
</abstract>
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