Cargando…
BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process
High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individ...
Autores principales: | Boukouvalas, Alexis, Hensman, James, Rattray, Magnus |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975664/ https://www.ncbi.nlm.nih.gov/pubmed/29843817 http://dx.doi.org/10.1186/s13059-018-1440-2 |
Ejemplares similares
-
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments
por: BinTayyash, Nuha, et al.
Publicado: (2021) -
GrandPrix: scaling up the Bayesian GPLVM for single-cell data
por: Ahmed, Sumon, et al.
Publicado: (2019) -
SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data
por: Welch, Joshua D., et al.
Publicado: (2016) -
BGP in the data center
por: Dutt, Dinesh G
Publicado: (2017) -
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes
por: Phillips, Nick E., et al.
Publicado: (2017)