Cargando…
BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference
We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer ce...
Autores principales: | Rahmani, Elior, Schweiger, Regev, Shenhav, Liat, Wingert, Theodora, Hofer, Ira, Gabel, Eilon, Eskin, Eleazar, Halperin, Eran |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6151042/ https://www.ncbi.nlm.nih.gov/pubmed/30241486 http://dx.doi.org/10.1186/s13059-018-1513-2 |
Ejemplares similares
-
Association testing of bisulfite-sequencing methylation data via a Laplace approximation
por: Weissbrod, Omer, et al.
Publicado: (2017) -
Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
por: Rahmani, Elior, et al.
Publicado: (2019) -
Genome-wide methylation data mirror ancestry information
por: Rahmani, Elior, et al.
Publicado: (2017) -
The Effect of Model Directionality on Cell-Type-Specific Differential DNA Methylation Analysis
por: Rahmani, Elior, et al.
Publicado: (2022) -
CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets
por: Thompson, Mike, et al.
Publicado: (2019)