Visualizing Biclusters in Gene Expression Data
Author Information
Author(s): Gregory A Grothaus, Adeel Mufti, TM Murali
Primary Institution: Virginia Polytechnic Institute and State University
Hypothesis
Can we develop an algorithm to effectively layout and visualize biclusters in gene expression data?
Conclusion
The proposed algorithm effectively visualizes biclusters in gene expression data, revealing overlaps and relationships.
Supporting Evidence
- The algorithm was tested on gene expression data for two types of leukaemia and protein-DNA binding data.
- A web-based interface was developed to help users visualize and query the bicluster layouts.
- The layout algorithm runs efficiently, even with large numbers of biclusters.
Takeaway
This study created a new way to show groups of genes that work together in different conditions, making it easier to see how they relate.
Methodology
The study developed a novel algorithm for laying out biclusters in a two-dimensional matrix and created a web-based interface for visualization.
Limitations
The algorithm may struggle with very large datasets, making navigation difficult without the web interface.
Digital Object Identifier (DOI)
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