GENE DISCOVERY METHODS FROM LARGE-SCALE GENE EXPRESSION DATA

摘要

Microarrays provide genome-wide gene expression changes. In current analyses, the majority of genes on the array are frequently eliminated for further analysis just in order for computational effort to be affordable. This strategy risks failure to discover whole sets of genes related to a quantitative trait of interest, which is generally controlled by several loci that might be eliminated in current approaches. Here, we describe a high-throughput gene discovery method based on correspondence analysis with a new index for expression ratios [arctan (1/ratio)] and three artificial marker genes. This method allows us to quickly analyze the whole microarray dataset without elimination and discover up/down-regulated genes related to a trait of interest. We employed an example dataset to show the theoretical advantage of this method. We then used the method to identify 88 cancer-related genes from a published microarray data from patients with breast cancer. This method can be easily performed and the result is also visible in three-dimensional viewing software that we have developed. Our method is useful for revaluating the wealth of microarray data available from web-sites.