Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes

<p>Abstract</p> <p>Background</p> <p>The landscape of biological and biomedical research is being changed rapidly with the invention of microarrays which enables simultaneous view on the transcription levels of a huge number of genes across different experimental condit...

Full description

Bibliographic Details
Main Authors: Bandyopadhyay Sanghamitra, Mukhopadhyay Anirban, Maulik Ujjwal
Format: Article
Language:English
Published: BMC 2009-01-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/10/27
id doaj-9b367368281b4b768a2364abcbfb4891
record_format Article
spelling doaj-9b367368281b4b768a2364abcbfb48912020-11-24T21:27:20ZengBMCBMC Bioinformatics1471-21052009-01-011012710.1186/1471-2105-10-27Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genesBandyopadhyay SanghamitraMukhopadhyay AnirbanMaulik Ujjwal<p>Abstract</p> <p>Background</p> <p>The landscape of biological and biomedical research is being changed rapidly with the invention of microarrays which enables simultaneous view on the transcription levels of a huge number of genes across different experimental conditions or time points. Using microarray data sets, clustering algorithms have been actively utilized in order to identify groups of co-expressed genes. This article poses the problem of fuzzy clustering in microarray data as a multiobjective optimization problem which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. Each of these clustering solutions possesses some amount of information regarding the clustering structure of the input data. Motivated by this fact, a novel fuzzy majority voting approach is proposed to combine the clustering information from all the solutions in the resultant Pareto-optimal set. This approach first identifies the genes which are assigned to some particular cluster with high membership degree by most of the Pareto-optimal solutions. Using this set of genes as the training set, the remaining genes are classified by a supervised learning algorithm. In this work, we have used a Support Vector Machine (SVM) classifier for this purpose.</p> <p>Results</p> <p>The performance of the proposed clustering technique has been demonstrated on five publicly available benchmark microarray data sets, viz., Yeast Sporulation, Yeast Cell Cycle, Arabidopsis Thaliana, Human Fibroblasts Serum and Rat Central Nervous System. Comparative studies of the use of different SVM kernels and several widely used microarray clustering techniques are reported. Moreover, statistical significance tests have been carried out to establish the statistical superiority of the proposed clustering approach. Finally, biological significance tests have been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant clusters of co-expressed genes.</p> <p>Conclusion</p> <p>The proposed clustering method has been shown to perform better than other well-known clustering algorithms in finding clusters of co-expressed genes efficiently. The clusters of genes produced by the proposed technique are also found to be biologically significant, i.e., consist of genes which belong to the same functional groups. This indicates that the proposed clustering method can be used efficiently to identify co-expressed genes in microarray gene expression data.</p> <p><b>Supplementary Website </b>The pre-processed and normalized data sets, the matlab code and other related materials are available at <url>http://anirbanmukhopadhyay.50webs.com/mogasvm.html</url>.</p> http://www.biomedcentral.com/1471-2105/10/27
collection DOAJ
language English
format Article
sources DOAJ
author Bandyopadhyay Sanghamitra
Mukhopadhyay Anirban
Maulik Ujjwal
spellingShingle Bandyopadhyay Sanghamitra
Mukhopadhyay Anirban
Maulik Ujjwal
Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes
BMC Bioinformatics
author_facet Bandyopadhyay Sanghamitra
Mukhopadhyay Anirban
Maulik Ujjwal
author_sort Bandyopadhyay Sanghamitra
title Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes
title_short Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes
title_full Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes
title_fullStr Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes
title_full_unstemmed Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes
title_sort combining pareto-optimal clusters using supervised learning for identifying co-expressed genes
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2009-01-01
description <p>Abstract</p> <p>Background</p> <p>The landscape of biological and biomedical research is being changed rapidly with the invention of microarrays which enables simultaneous view on the transcription levels of a huge number of genes across different experimental conditions or time points. Using microarray data sets, clustering algorithms have been actively utilized in order to identify groups of co-expressed genes. This article poses the problem of fuzzy clustering in microarray data as a multiobjective optimization problem which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. Each of these clustering solutions possesses some amount of information regarding the clustering structure of the input data. Motivated by this fact, a novel fuzzy majority voting approach is proposed to combine the clustering information from all the solutions in the resultant Pareto-optimal set. This approach first identifies the genes which are assigned to some particular cluster with high membership degree by most of the Pareto-optimal solutions. Using this set of genes as the training set, the remaining genes are classified by a supervised learning algorithm. In this work, we have used a Support Vector Machine (SVM) classifier for this purpose.</p> <p>Results</p> <p>The performance of the proposed clustering technique has been demonstrated on five publicly available benchmark microarray data sets, viz., Yeast Sporulation, Yeast Cell Cycle, Arabidopsis Thaliana, Human Fibroblasts Serum and Rat Central Nervous System. Comparative studies of the use of different SVM kernels and several widely used microarray clustering techniques are reported. Moreover, statistical significance tests have been carried out to establish the statistical superiority of the proposed clustering approach. Finally, biological significance tests have been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant clusters of co-expressed genes.</p> <p>Conclusion</p> <p>The proposed clustering method has been shown to perform better than other well-known clustering algorithms in finding clusters of co-expressed genes efficiently. The clusters of genes produced by the proposed technique are also found to be biologically significant, i.e., consist of genes which belong to the same functional groups. This indicates that the proposed clustering method can be used efficiently to identify co-expressed genes in microarray gene expression data.</p> <p><b>Supplementary Website </b>The pre-processed and normalized data sets, the matlab code and other related materials are available at <url>http://anirbanmukhopadhyay.50webs.com/mogasvm.html</url>.</p>
url http://www.biomedcentral.com/1471-2105/10/27
work_keys_str_mv AT bandyopadhyaysanghamitra combiningparetooptimalclustersusingsupervisedlearningforidentifyingcoexpressedgenes
AT mukhopadhyayanirban combiningparetooptimalclustersusingsupervisedlearningforidentifyingcoexpressedgenes
AT maulikujjwal combiningparetooptimalclustersusingsupervisedlearningforidentifyingcoexpressedgenes
_version_ 1725975300772200448