omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data
Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv, simplifying the application, benchmarking, and optimization of deconvolution methods.
Authors
- Frank Edenhofer (ORCID: https://orcid.org/0000-0002-6489-714X)
- K. Pelz (ORCID: https://orcid.org/0009-0003-3891-2005)
- Gregor Sturm (ORCID: https://orcid.org/0000-0001-9584-7842)
- Lorenzo Merotto (ORCID: https://orcid.org/0009-0006-0637-8436)
- Constantin Zackl (ORCID: https://orcid.org/0000-0003-1991-6943)
- Bernhard Eder
- Katharina Reinisch
- Alexander Dietrich
- Federico Marini
- Markus List
- Francesca Finotello
Institutions
- Boehringer Ingelheim (Germany) (DE)
- Innsbruck Medical University (AT)
- Universität Innsbruck (AT)
- Johannes Gutenberg University Mainz (DE)
- University Medical Center of the Johannes Gutenberg University Mainz (DE)
- Immune Regulation (United Kingdom) (GB)
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Genome biology
- Published
- 2026-01-26
- DOI
- https://doi.org/10.1186/s13059-026-03955-w
- Citations
- 3
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 66.71