Goertler, F.; Voelkl, D.; Bolz, S.; Rayford, A.; Stevenson, T.; Sterr, T.; Mensching-Buhr, M.; Seifert, N.; Altenbuchinger, M.; Arp, J.; Schuster, C.; Tausche, J.; Engel, L.; Zacharias, H. U.

Most deconvolution methods estimate cellular composition at a single level of cellular resolution despite biological processes often manifesting within fine-grained cellular subpopulations. We present HIDE-Deconv, a hierarchical deconvolution framework that jointly optimizes cellular compositions across multiple levels of a cell-type hierarchy while maintaining consistency between resolutions. In benchmark experiments, HIDE-Deconv achieved the highest overall predictive performance among evaluated methods. Analyses of lung adenocarcinoma, sepsis, COVID-19 and systemic lupus erythematosus revealed biologically relevant cellular remodeling that remained concealed at broader levels of cellular resolution. HIDE-Deconv is available as an open-source framework at https://github.com/dvoelkl/HIDE-deconv.