To achieve these comparisons, we used one linear model with a nested variable capturing the interaction between cell type, OUD diagnosis, Sex, and Region, Celltype_Dx_Sex_Region. The nested variable allowed for contrasting subsets of the data to calculate differential expression for each family of hypotheses at different cell type resolutions:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{Gene}}}}}}\_{{{{{\rm{expression}}}}}} \sim 0+{{{{{\rm{Celltype}}}}}}\_{{{{{\rm{Dx}}}}}}\_{{{{{\rm{Sex}}}}}}\_{{{{{\rm{Region}}}}}}+{{{{{\rm{Age}}}}}}+{{{{{\rm{PMI}}}}}}+{{{{{\rm{RIN}}}}}} \\ +{{{{{\rm{GDR}}}}}}+23{{{{{\rm{SVs}}}}}},$$\end{document}Gene_expression~0+Celltype_Dx_Sex_Region+Age+PMI+RIN+GDR+23SVs,
