Cargando…
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine hetero...
Autores principales: | Vallejos, Catalina A., Marioni, John C., Richardson, Sylvia |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4480965/ https://www.ncbi.nlm.nih.gov/pubmed/26107944 http://dx.doi.org/10.1371/journal.pcbi.1004333 |
Ejemplares similares
-
Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data
por: Eling, Nils, et al.
Publicado: (2018) -
Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data
por: Eling, Nils, et al.
Publicado: (2019) -
Beyond comparisons of means: understanding changes in gene expression at the single-cell level
por: Vallejos, Catalina A., et al.
Publicado: (2016) -
SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics data
por: Maniatis, Christos, et al.
Publicado: (2022) -
scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution
por: Kapourani, Chantriolnt-Andreas, et al.
Publicado: (2021)