Cargando…
Unsupervised embedding of single-cell Hi-C data
MOTIVATION: Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell-cycle axes. However, Hi-C data analysis requires methods that take into acco...
Autores principales: | Liu, Jie, Lin, Dejun, Yardımcı, Galip Gürkan, Noble, William Stafford |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022597/ https://www.ncbi.nlm.nih.gov/pubmed/29950005 http://dx.doi.org/10.1093/bioinformatics/bty285 |
Ejemplares similares
-
Software tools for visualizing Hi-C data
por: Yardımcı, Galip Gürkan, et al.
Publicado: (2017) -
Haplotype phasing in single-cell DNA-sequencing data
por: Satas, Gryte, et al.
Publicado: (2018) -
Random forest based similarity learning for single cell RNA sequencing data
por: Pouyan, Maziyar Baran, et al.
Publicado: (2018) -
A scalable estimator of SNP heritability for biobank-scale data
por: Wu, Yue, et al.
Publicado: (2018) -
Covariate-dependent negative binomial factor analysis of RNA sequencing data
por: Zamani Dadaneh, Siamak, et al.
Publicado: (2018)