Summary
Using 3D chromatin assays, we generated an extensive enhancer interaction dataset for the human pancreas, spanning more than 20 donors and five major cell types, including both the exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating quantitative, genome-wide analysis of enhancer connectivity. Using the tree models, we developed a machine learning algorithm capable of estimating the impact of enhancer perturbations on cell-type specific gene expression in the human pancreas. Complementing this compu
