A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies

With the development of large scale single cell RNA-seq technology, population-scale scRNA-seq studies are emerging. Here, the authors develop BAMM-SC, a tool for clustering droplet-based scRNA-seq data from multiple individuals simultaneously.

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Autores principales: Zhe Sun, Li Chen, Hongyi Xin, Yale Jiang, Qianhui Huang, Anthony R. Cillo, Tracy Tabib, Jay K. Kolls, Tullia C. Bruno, Robert Lafyatis, Dario A. A. Vignali, Kong Chen, Ying Ding, Ming Hu, Wei Chen
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Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/52d49e5908374e5ea58127ece365e62a
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spelling oai:doaj.org-article:52d49e5908374e5ea58127ece365e62a2021-12-02T14:38:42ZA Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies10.1038/s41467-019-09639-32041-1723https://doaj.org/article/52d49e5908374e5ea58127ece365e62a2019-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-09639-3https://doaj.org/toc/2041-1723With the development of large scale single cell RNA-seq technology, population-scale scRNA-seq studies are emerging. Here, the authors develop BAMM-SC, a tool for clustering droplet-based scRNA-seq data from multiple individuals simultaneously.Zhe SunLi ChenHongyi XinYale JiangQianhui HuangAnthony R. CilloTracy TabibJay K. KollsTullia C. BrunoRobert LafyatisDario A. A. VignaliKong ChenYing DingMing HuWei ChenNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-10 (2019)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Zhe Sun
Li Chen
Hongyi Xin
Yale Jiang
Qianhui Huang
Anthony R. Cillo
Tracy Tabib
Jay K. Kolls
Tullia C. Bruno
Robert Lafyatis
Dario A. A. Vignali
Kong Chen
Ying Ding
Ming Hu
Wei Chen
A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies
description With the development of large scale single cell RNA-seq technology, population-scale scRNA-seq studies are emerging. Here, the authors develop BAMM-SC, a tool for clustering droplet-based scRNA-seq data from multiple individuals simultaneously.
format article
author Zhe Sun
Li Chen
Hongyi Xin
Yale Jiang
Qianhui Huang
Anthony R. Cillo
Tracy Tabib
Jay K. Kolls
Tullia C. Bruno
Robert Lafyatis
Dario A. A. Vignali
Kong Chen
Ying Ding
Ming Hu
Wei Chen
author_facet Zhe Sun
Li Chen
Hongyi Xin
Yale Jiang
Qianhui Huang
Anthony R. Cillo
Tracy Tabib
Jay K. Kolls
Tullia C. Bruno
Robert Lafyatis
Dario A. A. Vignali
Kong Chen
Ying Ding
Ming Hu
Wei Chen
author_sort Zhe Sun
title A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies
title_short A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies
title_full A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies
title_fullStr A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies
title_full_unstemmed A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies
title_sort bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies
publisher Nature Portfolio
publishDate 2019
url https://doaj.org/article/52d49e5908374e5ea58127ece365e62a
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