HIDE-Deconv: A hierarchical deconvolution framework for multiscale characterization of cellular remodeling

Authors

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.

Abstract

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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 27 Aug 2026.

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