Bayesian Hierarchical Model for Correcting Signal Saturation in Microarrays Using Pixel Intensities

Pixel saturation occurs when the pixel intensity exceeds the scanner upper threshold of detection and the recorded pixel intensity is then truncated at the threshold. Truncation of the pixel intensity causes the estimates of gene expression (i.e., intensity) to be biased. Microarray experiments are commonly affected by saturated pixels; as a result all higher level analyses are made on these biased gene expression estimates. In this paper, we propose a method for improving the quality of the signal for cDNA microarrays by making use of several scans at varying scanner sensitivities. For each spot, pixel level intensity readings are given as input to a Bayesian hierarchical model. The model uses the pixel intensities of the spot to provide a posterior distribution of the true expression level of the corresponding genes. The parameters of the hierarchical model are estimated jointly with these expression levels, thus performing an integrated analysis of the measurement data. The method improves in all ranges the accuracy with which intensities can be estimated and extends the dynamic range of measured gene expression at the high end. The method is generic and can be applied to data from any organism and for imaging with any scanner. Results from a real data set illustrate an improved precision in the estimation of the expression of genes compared to what can be achieved by applying standard methods and using only a single scan.