Orthology-based multilevel modeling of differentially expressed mouse and human gene pairs.

There is great interest in finding human genes expressed through pharmaceutical intervention, thus opening a genomic window into benefit and side-effect profiles of a drug. Human insight gained from FDA-required animal experiments has historically been limited, but in the case of gene expression measurements, proposed biological orthologies between mouse and human genes provide a foothold for animal-to-human extrapolation. We have investigated a five-component, multilevel, bivariate normal mixture model that incorporates mouse, as well as human, gene expression data. The goal is two-fold: to increase human differential gene-finding power; and to find a subclass of gene pairs for which there is a direct exploitable relationship between animal and human genes. In simulation studies, the dual-species model boasted impressive gains in differential gene-finding power over a related marginal model using only human data. Bias in parameter estimation was problematic, however, and occasionally led to failures in control of the false discovery rate. Though it was considerably more difficult to find species-extrapolative gene-pairs (than differentially expressed human genes), simulation experiments deemed it to be possible, especially when traditional FDR controls are relaxed and under hypothetical parameter configurations.