Evaluation of agreement between common clustering strategies for DNA methylation-based subtyping of breast tumours
Our study highlights that common clustering strategies involving an arbitrary choice of algorithm, number of clusters and number of methylation sites are likely to identify different DNA methylation-based breast tumor subtypes.
Authors
- Pierre‐Antoine Dugué (ORCID: https://orcid.org/0000-0003-2736-3023)
- Graham G. Giles (ORCID: https://orcid.org/0000-0003-4946-9099)
- Enes Makalic (ORCID: https://orcid.org/0000-0003-3017-0871)
- Roger Laughlin Milne (ORCID: https://orcid.org/0000-0001-5764-7268)
- Catriona McLean (ORCID: https://orcid.org/0000-0002-0302-5727)
- Melissa C. Southey (ORCID: https://orcid.org/0000-0002-6313-9005)
- Shuai Li (ORCID: https://orcid.org/0000-0002-8696-8594)
- Ee Ming Wong
- Elaheh Zarean
Institutions
- The University of Melbourne (AU)
- Cancer Council Victoria (AU)
- Australian Regenerative Medicine Institute (AU)
- The Alfred Hospital (AU)
- Monash Health (AU)
- Alfred Health (AU)
- Monash University (AU)
Publication Details
- Journal
- Epigenomics
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1080/17501911.2024.2441653
- Citations
- 3
- Primary Topic
- Epigenetics and DNA Methylation
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- National Health and Medical Research Council