We get final dispersion estimates from this model in three steps, which implement a computationally fast approximation to a full empirical Bayes treatment. We first use the count data for each gene separately to get preliminary gene-wise dispersion estimates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $\alpha ^{\text {gw}}_{i}$ \end{document}αigw by maximum-likelihood estimation. Then, we fit the dispersion trend αtr. Finally, we combine the likelihood with the trended prior to get maximum a posteriori (MAP) values as final dispersion estimates. Details for the three steps follow.
