Research Article
Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique
Haithem Aouabed,
Mourad Elloumi
Issue:
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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Views:
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.
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 characteri...
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