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A multi-center cross-platform single-cell RNA sequencing reference dataset
Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-se...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7854649/ https://www.ncbi.nlm.nih.gov/pubmed/33531477 http://dx.doi.org/10.1038/s41597-021-00809-x |
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author | Chen, Xin Yang, Zhaowei Chen, Wanqiu Zhao, Yongmei Farmer, Andrew Tran, Bao Furtak, Vyacheslav Moos, Malcolm Xiao, Wenming Wang, Charles |
author_facet | Chen, Xin Yang, Zhaowei Chen, Wanqiu Zhao, Yongmei Farmer, Andrew Tran, Bao Furtak, Vyacheslav Moos, Malcolm Xiao, Wenming Wang, Charles |
author_sort | Chen, Xin |
collection | PubMed |
description | Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmark scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. We believe the datasets we describe here will provide a resource that meets this need by allowing evaluation of various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis. |
format | Online Article Text |
id | pubmed-7854649 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78546492021-02-11 A multi-center cross-platform single-cell RNA sequencing reference dataset Chen, Xin Yang, Zhaowei Chen, Wanqiu Zhao, Yongmei Farmer, Andrew Tran, Bao Furtak, Vyacheslav Moos, Malcolm Xiao, Wenming Wang, Charles Sci Data Data Descriptor Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmark scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. We believe the datasets we describe here will provide a resource that meets this need by allowing evaluation of various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis. Nature Publishing Group UK 2021-02-02 /pmc/articles/PMC7854649/ /pubmed/33531477 http://dx.doi.org/10.1038/s41597-021-00809-x Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Chen, Xin Yang, Zhaowei Chen, Wanqiu Zhao, Yongmei Farmer, Andrew Tran, Bao Furtak, Vyacheslav Moos, Malcolm Xiao, Wenming Wang, Charles A multi-center cross-platform single-cell RNA sequencing reference dataset |
title | A multi-center cross-platform single-cell RNA sequencing reference dataset |
title_full | A multi-center cross-platform single-cell RNA sequencing reference dataset |
title_fullStr | A multi-center cross-platform single-cell RNA sequencing reference dataset |
title_full_unstemmed | A multi-center cross-platform single-cell RNA sequencing reference dataset |
title_short | A multi-center cross-platform single-cell RNA sequencing reference dataset |
title_sort | multi-center cross-platform single-cell rna sequencing reference dataset |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7854649/ https://www.ncbi.nlm.nih.gov/pubmed/33531477 http://dx.doi.org/10.1038/s41597-021-00809-x |
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