GAM-clustering provides metabolic variability within dataset using a novel network-based computational approach that utilizes cellular transcriptional profiles as proxies. The metabolic network of reactions from KEGG database is presented as a graph that has vertices corresponding to metabolites and the edges corresponding to the reactions with the expressed genes. In the graph the method tries to find a set of connected subgraphs, with each corresponding well to a certain gene expression pattern. Curret analysis reveals the major metabolic features associated with different subpopulations and highlights a number of metabolic modules that are specific to individual cell types, tissues of residence, or developmental stages.
How to cite this article: Gainullina A. et al. Open Source ImmGen: network perspective on metabolic diversity among mononuclear phagocytes, bioRxiv 2020.07.15.204388; doi: https://doi.org/10.1101/2020.07.15.204388
GitHub repository contains the basic scripts and data analysis example. We plan to further extend and update this repository.