Mining Association Rules in Microarray Data

Microarray technology arguably has created a revolution in the field of biomedical research, providing researchers with a means of looking at how genes are expressed under certain physical conditions such as drug treatment or disease. Although extremely powerful, significant obstacles stand in the way of fully utilizing this data. Many data mining techniques have been applied to microarray data analysis including hierarchical clustering, k-means clustering, and self organizing maps but very little literature exists on the application of association rules to microarray data. Association rules not only allow us to group similarly expressed genes but also help discern relationships between genes. Introduction Up until the recent past, researchers were only able to examine the expression levels of one or a few genes at a time but with the advent of Microarray technology, up to 20,000+ genes can be observed at once. The power of Microarray is in determining relationships between genes, those that are differentially or coordinately expressed under specific conditions. Such genes may provide possible drug target candidates or aide in the understanding of a disease process. Microarray experiments produce vast amounts of data requiring advanced data analysis techniques. Biology Primer The central Dogma of Biology states that the flow of information is from genetic material, DNA, to RNA, to protein. The nucleus of each cell contains DNA coding for all proteins found in the body. Depending on its environment, a cell will express only a certain number of genes by translating the double stranded DNA to single stranded RNA through the process of transcription. The RNA is then transferred out of the nucleus of the cell into the cytoplasm where the cells protein production machinery takes over and produces proteins from the RNA blueprints through the process of translation. A cell can regulate the amount of protein present by up-regulating or down-regulating the production of RNA which inevitably increases of decrease the amount of protein produced. Proteins are responsible for almost all biological processes in the body. Disease states can often be traced back to a change, for some reason, in the levels of certain proteins. Biotechnology drug development frequently tries to understand and target these protein interaction pathways. Microarray technology is able to look at the expression levels of thousands of genes at once. Microarray Process Primer A DNA microarray consists of a glass or nylon slide coated with many spots, each of which contains many identical DNA sequences known as probes. Each probe is a piece of single stranded complimentary DNA (cDNA) to a gene of interest. A researcher typically looks at expression levels in certain tissues. The tissue of interest is dissected from a drug treated, diseased, or control animal and RNA is extracted from it. Complimentary copies of all RNA present are produced and labeled with fluorescent dyes, producing labeled cDNA. A solution containing the labeled cDNA is introduced to the microarray chip. Through the base pairing principle described by Watson and Crick, the labeled cDNA binds to the cDNA on the chip with A binding to T and C binding to G. Therefore, the sequence AGTCTA would only bind to TCAGAT. Unbound cDNA is washed from the slide and the relative amounts of bound cDNA are determined by measuring the fluorescence of each spot. A series of data cleaning and normalization steps produces numerical data representing the expression levels of each gene compared to control. A value of 1 means no change, less than one means the gene is under expressed, and greater than one means the gene is over expressed (see Table 1).