We use GLMs with a logarithmic link, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $\log _{2} q_{\textit {ij}}=\sum _{r} x_{\textit {jr}} \beta _{\textit {ir}}$ \end{document}log2qij=∑rxjrβir, with design matrix elements xjr and coefficients βir. In the simplest case of a comparison between two groups, such as treated and control samples, the design matrix elements indicate whether a sample j is treated or not, and the GLM fit returns coefficients indicating the overall expression strength of the gene and the log 2 fold change between treatment and control. The use of linear models, however, provides the flexibility to also analyze more complex designs, as is often useful in genomic studies [15].
