Bayesian processing of microarray images

Abstract

Gene expression measurements quantify the level of mRNA produced from each gene. Two principal methods exist for producing slides for extracting these levels: photolithography and spotted arrays. One difficulty with the spotted array format is determining the size and location of the spots on the array. In this paper we present a Bayesian approach to processing images produced by these arrays that seeks posterior distributions over the size and positions of the spots. This enables us to estimate expression ratios and their variances. Exact inference for the model we specify is intractable; we develop an approximate inference technique, which combines importance sampling with variational inference. Our technique has already been shown to be more consistent than both manual processing and another automated technique [N. D. Lawrence, et al., "Reducing the Variability in cDNA Microarray Image Processing by Inference"]. Here we present large-scale results for twenty-four microarray slides each representing 5760 genes and show the dramatic effects of incorporating variance in our downstream analysis. Software based on this algorithm is available for academic use.