Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study

Md. Bipul Hossen; Md. Siraj-Ud-Doulah; Aminul Hoque

doi:doi:10.11648/j.cbb.20150306.12

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Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study

Md. Bipul Hossen, Md. Siraj-Ud-Doulah, Aminul Hoque

Published in Computational Biology and Bioinformatics (Volume 3, Issue 6)

Received: 5 December 2015 Accepted: 14 December 2015 Published: 30 December 2015

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Abstract

Microarray is already well established techniques to understand various cellular functions by profiling transcriptomics data. To capture the overall feature of high dimensional variable datasets in microarray data, various analytical and statistical approaches are already developed. One of the most widely used Agglomerative Hierarchical Clustering (AHC) methods is the cluster analysis of gene expression data; however, little work has been done to compare the performance of clustering methods on gene expression data, where some authors used three or four AHC methods and some others used at most five AHC methods. All of the authors concretely suggested complete linkage method to further researchers to determine the best method for clustering their gene expression data. This paper compared the performance of seven AHC methods for clustering gene expression data with respect to five major proximity measures. We used corrected Rand (cR) Index to compare the performance of each clustering method. To illustrate the results, we found that the clustering method Ward exhibited the best performance among all of the AHC methods as well as the proximity measure Cosine performed better in comparison to all the other measures in both type of Affymetrix and cDNA datasets.

Published in	Computational Biology and Bioinformatics (Volume 3, Issue 6)
DOI	10.11648/j.cbb.20150306.12
Page(s)	88-94
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Agglomerative Hierarchical Clustering, Proximity Measures, Corrected Rand Index, Gene Expressions Data

References

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APA Style

Md. Bipul Hossen, Md. Siraj-Ud-Doulah, Aminul Hoque. (2015). Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study. Computational Biology and Bioinformatics, 3(6), 88-94. https://doi.org/10.11648/j.cbb.20150306.12

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ACS Style

Md. Bipul Hossen; Md. Siraj-Ud-Doulah; Aminul Hoque. Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study. Comput. Biol. Bioinform. 2015, 3(6), 88-94. doi: 10.11648/j.cbb.20150306.12

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AMA Style

Md. Bipul Hossen, Md. Siraj-Ud-Doulah, Aminul Hoque. Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study. Comput Biol Bioinform. 2015;3(6):88-94. doi: 10.11648/j.cbb.20150306.12

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@article{10.11648/j.cbb.20150306.12,
  author = {Md. Bipul Hossen and Md. Siraj-Ud-Doulah and Aminul Hoque},
  title = {Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study},
  journal = {Computational Biology and Bioinformatics},
  volume = {3},
  number = {6},
  pages = {88-94},
  doi = {10.11648/j.cbb.20150306.12},
  url = {https://doi.org/10.11648/j.cbb.20150306.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20150306.12},
  abstract = {Microarray is already well established techniques to understand various cellular functions by profiling transcriptomics data. To capture the overall feature of high dimensional variable datasets in microarray data, various analytical and statistical approaches are already developed. One of the most widely used Agglomerative Hierarchical Clustering (AHC) methods is the cluster analysis of gene expression data; however, little work has been done to compare the performance of clustering methods on gene expression data, where some authors used three or four AHC methods and some others used at most five AHC methods. All of the authors concretely suggested complete linkage method to further researchers to determine the best method for clustering their gene expression data. This paper compared the performance of seven AHC methods for clustering gene expression data with respect to five major proximity measures. We used corrected Rand (cR) Index to compare the performance of each clustering method. To illustrate the results, we found that the clustering method Ward exhibited the best performance among all of the AHC methods as well as the proximity measure Cosine performed better in comparison to all the other measures in both type of Affymetrix and cDNA datasets.},
 year = {2015}
}

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TY  - JOUR
T1  - Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study
AU  - Md. Bipul Hossen
AU  - Md. Siraj-Ud-Doulah
AU  - Aminul Hoque
Y1  - 2015/12/30
PY  - 2015
N1  - https://doi.org/10.11648/j.cbb.20150306.12
DO  - 10.11648/j.cbb.20150306.12
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 88
EP  - 94
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20150306.12
AB  - Microarray is already well established techniques to understand various cellular functions by profiling transcriptomics data. To capture the overall feature of high dimensional variable datasets in microarray data, various analytical and statistical approaches are already developed. One of the most widely used Agglomerative Hierarchical Clustering (AHC) methods is the cluster analysis of gene expression data; however, little work has been done to compare the performance of clustering methods on gene expression data, where some authors used three or four AHC methods and some others used at most five AHC methods. All of the authors concretely suggested complete linkage method to further researchers to determine the best method for clustering their gene expression data. This paper compared the performance of seven AHC methods for clustering gene expression data with respect to five major proximity measures. We used corrected Rand (cR) Index to compare the performance of each clustering method. To illustrate the results, we found that the clustering method Ward exhibited the best performance among all of the AHC methods as well as the proximity measure Cosine performed better in comparison to all the other measures in both type of Affymetrix and cDNA datasets.
VL  - 3
IS  - 6
ER  -

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Author Information

Md. Bipul Hossen

Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh
Md. Siraj-Ud-Doulah

Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh
Aminul Hoque

Department of Statistics, Rajshahi University, Rajshahi, Bangladesh

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Plain Text BibTeX RIS

APA Style

Md. Bipul Hossen, Md. Siraj-Ud-Doulah, Aminul Hoque. (2015). Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study. Computational Biology and Bioinformatics, 3(6), 88-94. https://doi.org/10.11648/j.cbb.20150306.12

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ACS Style

Md. Bipul Hossen; Md. Siraj-Ud-Doulah; Aminul Hoque. Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study. Comput. Biol. Bioinform. 2015, 3(6), 88-94. doi: 10.11648/j.cbb.20150306.12

Copy | Download

AMA Style

Md. Bipul Hossen, Md. Siraj-Ud-Doulah, Aminul Hoque. Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study. Comput Biol Bioinform. 2015;3(6):88-94. doi: 10.11648/j.cbb.20150306.12

Copy | Download

@article{10.11648/j.cbb.20150306.12,
  author = {Md. Bipul Hossen and Md. Siraj-Ud-Doulah and Aminul Hoque},
  title = {Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study},
  journal = {Computational Biology and Bioinformatics},
  volume = {3},
  number = {6},
  pages = {88-94},
  doi = {10.11648/j.cbb.20150306.12},
  url = {https://doi.org/10.11648/j.cbb.20150306.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20150306.12},
  abstract = {Microarray is already well established techniques to understand various cellular functions by profiling transcriptomics data. To capture the overall feature of high dimensional variable datasets in microarray data, various analytical and statistical approaches are already developed. One of the most widely used Agglomerative Hierarchical Clustering (AHC) methods is the cluster analysis of gene expression data; however, little work has been done to compare the performance of clustering methods on gene expression data, where some authors used three or four AHC methods and some others used at most five AHC methods. All of the authors concretely suggested complete linkage method to further researchers to determine the best method for clustering their gene expression data. This paper compared the performance of seven AHC methods for clustering gene expression data with respect to five major proximity measures. We used corrected Rand (cR) Index to compare the performance of each clustering method. To illustrate the results, we found that the clustering method Ward exhibited the best performance among all of the AHC methods as well as the proximity measure Cosine performed better in comparison to all the other measures in both type of Affymetrix and cDNA datasets.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study
AU  - Md. Bipul Hossen
AU  - Md. Siraj-Ud-Doulah
AU  - Aminul Hoque
Y1  - 2015/12/30
PY  - 2015
N1  - https://doi.org/10.11648/j.cbb.20150306.12
DO  - 10.11648/j.cbb.20150306.12
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 88
EP  - 94
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20150306.12
AB  - Microarray is already well established techniques to understand various cellular functions by profiling transcriptomics data. To capture the overall feature of high dimensional variable datasets in microarray data, various analytical and statistical approaches are already developed. One of the most widely used Agglomerative Hierarchical Clustering (AHC) methods is the cluster analysis of gene expression data; however, little work has been done to compare the performance of clustering methods on gene expression data, where some authors used three or four AHC methods and some others used at most five AHC methods. All of the authors concretely suggested complete linkage method to further researchers to determine the best method for clustering their gene expression data. This paper compared the performance of seven AHC methods for clustering gene expression data with respect to five major proximity measures. We used corrected Rand (cR) Index to compare the performance of each clustering method. To illustrate the results, we found that the clustering method Ward exhibited the best performance among all of the AHC methods as well as the proximity measure Cosine performed better in comparison to all the other measures in both type of Affymetrix and cDNA datasets.
VL  - 3
IS  - 6
ER  -

Copy | Download