CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data
The development of CanSig facilitates computational strategies for the reproducible discovery of shared cancer cell states, improving cross-study reliability and enabling the detection of clinically relevant gene signatures. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .
Authors
- Paweł Czyż (ORCID: https://orcid.org/0000-0002-6262-0614)
- Niko Beerenwinkel (ORCID: https://orcid.org/0000-0002-0573-6119)
- Josephine Yates (ORCID: https://orcid.org/0000-0001-8346-4502)
- Agnieszka Kraft (ORCID: https://orcid.org/0000-0001-7672-2032)
- Marc Glettig (ORCID: https://orcid.org/0000-0002-2512-7740)
- Valentina Boeva (ORCID: https://orcid.org/0000-0002-4382-7185)
- Florian Barkmann (ORCID: https://orcid.org/0000-0003-1317-8177)
Institutions
- Broad Institute (US)
- Délégation Paris 5 (FR)
- SIB Swiss Institute of Bioinformatics (CH)
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Université Paris Cité (FR)
- ETH Zurich (CH)
- Institute for Biomedical Engineering (CH)
- The Geneva Association (CH)
- Institut Cochin (FR)
- École Polytechnique Fédérale de Lausanne (CH)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-11-13
- DOI
- https://doi.org/10.1158/0008-5472.can-25-0940
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Eidgenössische Technische Hochschule Zürich