Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique

Haithem Aouabed; Mourad Elloumi

doi:doi:10.11648/j.cbb.20231102.11

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Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique

Haithem Aouabed, Mourad Elloumi

Published in Computational Biology and Bioinformatics (Volume 11, Issue 2)

Received: 24 September 2023 Accepted: 12 October 2023 Published: 30 October 2023

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Abstract

Biclustering is a data mining technique used to analyze gene expression data. It consists of classifying subgroups of genes that behave similarly under subgroups of conditions and can behave independently under other conditions. These discovered co-expressed genes (called biclusters) can help to find specific biological aims like finding characteristics of a specific disease. A large number of biclustering algorithms have been developed. Generally, these algorithms give as output a large number of overlapped biclusters. The visualization of these biclusters is still a non-trivial task. In this paper, we present a new approach to display biclustering results from gene expression data on the same screen. It is based on a two-dimensional matrix where each bicluster is represented as a column and each overlap between a set of biclusters is represented as a row. We illustrated the usefulness of our method with biclustering results from real and synthetic datasets and we compared it to other techniques that concentrate on biclustering overlaps issue. The method is implemented in a web-based interactive visualization tool called VisBicluster available at http://vis.usal.es/~visusal/visbicluster.

Published in	Computational Biology and Bioinformatics (Volume 11, Issue 2)
DOI	10.11648/j.cbb.20231102.11
Page(s)	19-32
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), 2023. Published by Science Publishing Group

Keywords

Biclustering Visualization, Two-Dimensional Matrix, Filtering, Overlaps, InfoVis

References

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APA Style

Haithem Aouabed, Mourad Elloumi. (2023). Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Computational Biology and Bioinformatics, 11(2), 19-32. https://doi.org/10.11648/j.cbb.20231102.11

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ACS Style

Haithem Aouabed; Mourad Elloumi. Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Comput. Biol. Bioinform. 2023, 11(2), 19-32. doi: 10.11648/j.cbb.20231102.11

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AMA Style

Haithem Aouabed, Mourad Elloumi. Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Comput Biol Bioinform. 2023;11(2):19-32. doi: 10.11648/j.cbb.20231102.11

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@article{10.11648/j.cbb.20231102.11,
  author = {Haithem Aouabed and Mourad Elloumi},
  title = {Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique},
  journal = {Computational Biology and Bioinformatics},
  volume = {11},
  number = {2},
  pages = {19-32},
  doi = {10.11648/j.cbb.20231102.11},
  url = {https://doi.org/10.11648/j.cbb.20231102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20231102.11},
  abstract = {Biclustering is a data mining technique used to analyze gene expression data. It consists of classifying subgroups of genes that behave similarly under subgroups of conditions and can behave independently under other conditions. These discovered co-expressed genes (called biclusters) can help to find specific biological aims like finding characteristics of a specific disease. A large number of biclustering algorithms have been developed. Generally, these algorithms give as output a large number of overlapped biclusters. The visualization of these biclusters is still a non-trivial task. In this paper, we present a new approach to display biclustering results from gene expression data on the same screen. It is based on a two-dimensional matrix where each bicluster is represented as a column and each overlap between a set of biclusters is represented as a row. We illustrated the usefulness of our method with biclustering results from real and synthetic datasets and we compared it to other techniques that concentrate on biclustering overlaps issue. The method is implemented in a web-based interactive visualization tool called VisBicluster available at http://vis.usal.es/~visusal/visbicluster.},
 year = {2023}
}

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TY - JOUR
T1 - Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique
AU - Haithem Aouabed
AU - Mourad Elloumi
Y1 - 2023/10/30
PY - 2023
N1 - https://doi.org/10.11648/j.cbb.20231102.11
DO - 10.11648/j.cbb.20231102.11
T2 - Computational Biology and Bioinformatics
JF - Computational Biology and Bioinformatics
JO - Computational Biology and Bioinformatics
SP - 19
EP - 32
PB - Science Publishing Group
SN - 2330-8281
UR - https://doi.org/10.11648/j.cbb.20231102.11
AB - Biclustering is a data mining technique used to analyze gene expression data. It consists of classifying subgroups of genes that behave similarly under subgroups of conditions and can behave independently under other conditions. These discovered co-expressed genes (called biclusters) can help to find specific biological aims like finding characteristics of a specific disease. A large number of biclustering algorithms have been developed. Generally, these algorithms give as output a large number of overlapped biclusters. The visualization of these biclusters is still a non-trivial task. In this paper, we present a new approach to display biclustering results from gene expression data on the same screen. It is based on a two-dimensional matrix where each bicluster is represented as a column and each overlap between a set of biclusters is represented as a row. We illustrated the usefulness of our method with biclustering results from real and synthetic datasets and we compared it to other techniques that concentrate on biclustering overlaps issue. The method is implemented in a web-based interactive visualization tool called VisBicluster available at http://vis.usal.es/~visusal/visbicluster.
VL - 11
IS - 2
ER -

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Author Information

Haithem Aouabed

Computer Science Department, Faculty of Economic Sciences and Management, University of Sfax, Sfax, Tunisia
Mourad Elloumi

Computer Science Department, Faculty of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia

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APA Style

Haithem Aouabed, Mourad Elloumi. (2023). Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Computational Biology and Bioinformatics, 11(2), 19-32. https://doi.org/10.11648/j.cbb.20231102.11

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ACS Style

Haithem Aouabed; Mourad Elloumi. Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Comput. Biol. Bioinform. 2023, 11(2), 19-32. doi: 10.11648/j.cbb.20231102.11

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AMA Style

Haithem Aouabed, Mourad Elloumi. Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Comput Biol Bioinform. 2023;11(2):19-32. doi: 10.11648/j.cbb.20231102.11

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@article{10.11648/j.cbb.20231102.11,
  author = {Haithem Aouabed and Mourad Elloumi},
  title = {Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique},
  journal = {Computational Biology and Bioinformatics},
  volume = {11},
  number = {2},
  pages = {19-32},
  doi = {10.11648/j.cbb.20231102.11},
  url = {https://doi.org/10.11648/j.cbb.20231102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20231102.11},
  abstract = {Biclustering is a data mining technique used to analyze gene expression data. It consists of classifying subgroups of genes that behave similarly under subgroups of conditions and can behave independently under other conditions. These discovered co-expressed genes (called biclusters) can help to find specific biological aims like finding characteristics of a specific disease. A large number of biclustering algorithms have been developed. Generally, these algorithms give as output a large number of overlapped biclusters. The visualization of these biclusters is still a non-trivial task. In this paper, we present a new approach to display biclustering results from gene expression data on the same screen. It is based on a two-dimensional matrix where each bicluster is represented as a column and each overlap between a set of biclusters is represented as a row. We illustrated the usefulness of our method with biclustering results from real and synthetic datasets and we compared it to other techniques that concentrate on biclustering overlaps issue. The method is implemented in a web-based interactive visualization tool called VisBicluster available at http://vis.usal.es/~visusal/visbicluster.},
 year = {2023}
}

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