Expression ratio statistics and its applications to microarray data analysis

Microarray technology makes it possible to monitor expression levels of thousands of genes simultaneously during single or multiple experiments. Routinely, in order to analyze gene expressions level quantitatively, two fluorescent-labeled RNAs are hybridized to an array of cDNA probes on a glass slide. Ratios of gene expression levels arising from two co-hybridized samples are obtained through image segmentation and signal detection methods. During the past three years, we have developed a gene expression analysis system in which ratio statistics have been applied to expression analysis, and a ratio confidence interval has been established to identify ratio outliers. By using local background subtraction and weak target elimination, we have been able to assume that the fluorescent background level does not interfere with ratio measurement; however, experience suggests that ratios derived from either weak targets or in regions of high local background possess greater variation than those from strong targets. This paper proposes a new interaction model between fluorescent background and hybridization signals in which ratio statistics are numerically evaluated and a self-adjusting confidence interval is employed. The self-adjusting confidence interval, which automatically adapts under different signal-to-background ratios, provides a better criterion to further interrogate weak expression levels.