A Bayesian Model That Links Microarray mRNA Measurements to Mass Spectrometry Protein Measurements

Abstract

An important problem in biology is to understand correspondences between mRNA microarray levels and mass spectrometry peptide counts. Recently, a compendium of mRNA expression levels and protein abundances were released for the entire genome of the laboratory mouse, Mus musculus. The availability of these two data sets facilitate using machine learning methods to automatically infer plausible correspondences between the gene products. Knowing these correspondences can be helpful either for predicting protein abundances from microarray data or as an independent source of information that can be used for learning richer models such as regulatory networks. We propose a probabilistic model that relates protein abundances to mRNA expression levels. Using cross-mapped data from the above-mentioned studies, we learn the model and then score the genes for their strength of relationship by performing probabilistic inference in the learned model. While we gave a simplified outline of our technique in a publication aimed at biologists (Cell 2006), in this paper, we give a complete description of the Bayesian model and the computational technique used to perform inference. In addition, we demonstrate that the Bayesian technique achieves mappings with higher statistical significance, compared to standard linear regression and a maximum likelihood version of the proposed model.