Bayesian approach to single-cell differential expression analysis

Single-cell data provides means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression magni...

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Detalles Bibliográficos
Autores principales: Kharchenko, Peter V., Silberstein, Lev, Scadden, David T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2014
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112276/
https://www.ncbi.nlm.nih.gov/pubmed/24836921
http://dx.doi.org/10.1038/nmeth.2967
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author Kharchenko, Peter V.
Silberstein, Lev
Scadden, David T.
author_facet Kharchenko, Peter V.
Silberstein, Lev
Scadden, David T.
author_sort Kharchenko, Peter V.
collection PubMed
description Single-cell data provides means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression magnitude distortions typical of single-cell RNA sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.
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spelling pubmed-41122762015-01-01 Bayesian approach to single-cell differential expression analysis Kharchenko, Peter V. Silberstein, Lev Scadden, David T. Nat Methods Article Single-cell data provides means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression magnitude distortions typical of single-cell RNA sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise. 2014-05-18 2014-07 /pmc/articles/PMC4112276/ /pubmed/24836921 http://dx.doi.org/10.1038/nmeth.2967 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Kharchenko, Peter V.
Silberstein, Lev
Scadden, David T.
Bayesian approach to single-cell differential expression analysis
title Bayesian approach to single-cell differential expression analysis
title_full Bayesian approach to single-cell differential expression analysis
title_fullStr Bayesian approach to single-cell differential expression analysis
title_full_unstemmed Bayesian approach to single-cell differential expression analysis
title_short Bayesian approach to single-cell differential expression analysis
title_sort bayesian approach to single-cell differential expression analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112276/
https://www.ncbi.nlm.nih.gov/pubmed/24836921
http://dx.doi.org/10.1038/nmeth.2967
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