Gene Expression Profiling of DNA Microarray Data using Peano Count Trees (P-Trees)

The explosion of genomic data made possible by advances in parallel, high-throughput technologies in the area of molecular biology, has ushered in a new era in the area of Bioinformatics. During the last many years, efforts concentrated on sequencing the genome of organisms. Current emphasis lies in extracting meaningful information from this huge DNA sequence and expression data. The techniques currently employed to do analysis of microarray expression data is clustering and classification. These techniques present their own limitations as to the amount of useful information that can be derived. In this paper, we propose a new approach to data mining the microarray data using new data mining technology called Peano Count Tree (P-tree) 1 . This technology employs Association Rule Mining as means to do data mining of the microarray data. Association Rule Mining is an advanced data mining technique that is useful in deriving meaningful rules from a given data. We propose using Association Rule Mining to derive meaningful rules from microarray expression data. Our approach proposes a new microarray data mining technology, which involves a "Data Mining Ready" data structure, called Peano count tree (P-tree), to measure gene expression levels. The method involves treating the microarray data as spatial P-tree technology is patented to North Dakota State University Proc. of the Virt. Conf. in Genom. and Bioinf.© www.ndsu.edu/virtual-genomics North Dakota State University, USA Octuber, 15-16, 2001. data. Each spot on the microarray is presented as a pixel with corresponding red and green ratios. The microarray data is reorganized into an 8-bit bSQ file (where each attribute or band is stored as a separate file). Each bit is then converted in a quadrant base tree structure P-tree from which a data cube is constructed and meaningful rules readily obtained.