Interactive Gene Clustering--A Case Study of Breast Cancer Microarray Data

Authors:
Alicja Gruźdź;Aleksandra Ihnatowicz;Dominik Ślęzak
Affiliations:
Department of Computer Science, University of Regina, Regina, Canada S4S 0A2;Department of Computer Science, University of Regina, Regina, Canada S4S 0A2;Department of Computer Science, University of Regina, Regina, Canada S4S 0A2
Venue:
Information Systems Frontiers
Year:
2006

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Abstract

We present a new approach to clustering and visualization of the DNA microarray gene expression data. We utilize the self-organizing map (SOM) framework for handling (dis)similarities between genes in terms of their expression characteristics. We rely on appropriately defined distances between ranked genes-attributes, also capable of handling missing values. As a case study, we consider breast cancer data and the gene ESR1, whose expression alterations, appearing for many of the tumor subtypes, have been already observed to be correlated with some other significant genes. Preliminary results positively verify applicability of our approach, although further development is definitely needed. They suggest that it may be very effective when used by the domain experts. The algorithmic toolkit is enriched with GUI enabling the users to interactively support the SOM optimization process. Its effectiveness is achieved by drag&drop techniques allowing for the cluster modification according to the expert knowledge or intuition.