Non-negative matrix factorization and deconvolution as a dual simplex problem
The Dual Simplex unified analytical framework improves robustness to noise and enhances optimization stability, enabling accurate recovery of component proportions and expression profiles. Importantly, the framework naturally accommodates both reference-free and marker-based deconvolution settings, providing a general and efficient solution for analyzing complex biological mixtures such as bulk RNA-seq and single-cell derived data.
Authors
- Alexey Serdyukov (ORCID: https://orcid.org/0009-0004-4155-5237)
- Maxim N. Artyomov (ORCID: https://orcid.org/0000-0002-1133-4212)
- Denis Kleverov (ORCID: https://orcid.org/0009-0002-1362-486X)
- Ekaterina Aladyeva
Institutions
- ITMO University (RU)
- Washington University in St. Louis (US)
- Federal Almazov North-West Medical Research Centre (RU)
- The Ohio State University (US)
Publication Details
- Journal
- Genome biology
- Published
- 2026-01-14
- DOI
- https://doi.org/10.1186/s13059-025-03910-1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00