Fig. 1

Overview of the computational framework. Preparation: A change of variable (from expression values to Z-score) is used to detect otherwise hidden correlations between genes in single-cell datasets, ultimately allowing us to infer the global regulatory network. Gene centrality: Biological importance of genes is quantified using concepts from graph theory. Dynamical properties: We characterize the putative dynamical behavior of the regulatory networks by measuring the monotonicity. Applications: We generated, compared, and characterized the networks of 11 organs in the mouse (Tabula Muris), in the pancreas from healthy and type 2 diabetes human subjects, and in a mouse model of Alzheimer’s disease