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

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

Haithem Aouabed¹, Mourad Elloumi²

Corresponding Author: Haithem Aouabed

¹Computer Science Department, Faculty of Economic Sciences and Management, University of Sfax, Sfax, Tunisia

²Computer Science Department, Faculty of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia

Computational Biology and Bioinformatics, Volume 11, Issue 2, December 2023

Pages: 19-32

Received: Sep. 24, 2023

Accepted: Oct. 12, 2023

Published: Oct. 30, 2023

DOI: 10.11648/j.cbb.20231102.11

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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.

Keywords

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

To cite this article

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

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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