Cargando…

Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq

Summary: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data can be formalized under a general fram...

Descripción completa

Detalles Bibliográficos
Autores principales: Juliá, Miguel, Telenti, Amalio, Rausell, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595899/
https://www.ncbi.nlm.nih.gov/pubmed/26099264
http://dx.doi.org/10.1093/bioinformatics/btv368
_version_ 1782393692064055296
author Juliá, Miguel
Telenti, Amalio
Rausell, Antonio
author_facet Juliá, Miguel
Telenti, Amalio
Rausell, Antonio
author_sort Juliá, Miguel
collection PubMed
description Summary: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data can be formalized under a general framework composed of (i) a metric to assess cell-to-cell similarities (with or without a dimensionality reduction step) and (ii) a graph-building algorithm (optionally making use of a cell clustering step). The Sincell R package implements a methodological toolbox allowing flexible workflows under such a framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. The functionalities of Sincell are illustrated in a real case study, which demonstrates its ability to discriminate noisy from stable cell-state hierarchies. Availability and implementation: Sincell is an open-source R/Bioconductor package available at http://bioconductor.org/packages/sincell. A detailed manual and a vignette are provided with the package. Contact: antonio.rausell@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online.
format Online
Article
Text
id pubmed-4595899
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-45958992015-10-09 Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq Juliá, Miguel Telenti, Amalio Rausell, Antonio Bioinformatics Applications Notes Summary: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data can be formalized under a general framework composed of (i) a metric to assess cell-to-cell similarities (with or without a dimensionality reduction step) and (ii) a graph-building algorithm (optionally making use of a cell clustering step). The Sincell R package implements a methodological toolbox allowing flexible workflows under such a framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. The functionalities of Sincell are illustrated in a real case study, which demonstrates its ability to discriminate noisy from stable cell-state hierarchies. Availability and implementation: Sincell is an open-source R/Bioconductor package available at http://bioconductor.org/packages/sincell. A detailed manual and a vignette are provided with the package. Contact: antonio.rausell@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-10-15 2015-06-22 /pmc/articles/PMC4595899/ /pubmed/26099264 http://dx.doi.org/10.1093/bioinformatics/btv368 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Juliá, Miguel
Telenti, Amalio
Rausell, Antonio
Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq
title Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq
title_full Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq
title_fullStr Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq
title_full_unstemmed Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq
title_short Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq
title_sort sincell: an r/bioconductor package for statistical assessment of cell-state hierarchies from single-cell rna-seq
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595899/
https://www.ncbi.nlm.nih.gov/pubmed/26099264
http://dx.doi.org/10.1093/bioinformatics/btv368
work_keys_str_mv AT juliamiguel sincellanrbioconductorpackageforstatisticalassessmentofcellstatehierarchiesfromsinglecellrnaseq
AT telentiamalio sincellanrbioconductorpackageforstatisticalassessmentofcellstatehierarchiesfromsinglecellrnaseq
AT rausellantonio sincellanrbioconductorpackageforstatisticalassessmentofcellstatehierarchiesfromsinglecellrnaseq