Clustering Method to Identify Gene Sets with Similar Expression Profiles in Adjacent Chromosomal Regions

Authors:
Min A. Jhun;Taesung Park
Affiliations:
Bioinformatics Program, Seoul National University, Seoul, South Korea 151-742;Bioinformatics Program, Seoul National University, Seoul, South Korea 151-742 and Department of Statistics, Seoul National University, Seoul, South Korea 151-742
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Bioinformatics

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Abstract

The analysis of transcriptional data accounting for the chromosomal locations of genes can be applied to detecting gene sets sharing similar expression profiles in an adjacent chromosomal region. In this paper, we propose a new distance measure to integrate expression profiles with chromosomal locations. The performance of the proposed distance measure is evaluated via the bootstrap resampling procedure. We applied the proposed method to the microarray data in Drosophila genome and identified the set of genes of Toll and Imd pathway in adjacent chromosomal regions. Not only the proposed method gives stronger biological meaning to the clustering result, but also it provides biologically meaningful gene sets.