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Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA
Human Ensemble Cell Atlas (hECA) provides a unified informatics framework and the cell-centric-assembled single-cell transcriptome data of 1,093,299 labeled human cells from 116 published datasets. In this protocol, we provide three applications of hECA: “quantitative portraiture” exploration with w...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356166/ https://www.ncbi.nlm.nih.gov/pubmed/35942342 http://dx.doi.org/10.1016/j.xpro.2022.101589 |
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author | Chen, Yixin Hao, Minsheng Gao, Haoxiang Li, Jiaqi Chen, Sijie Li, Fanhong Wei, Lei Zhang, Xuegong |
author_facet | Chen, Yixin Hao, Minsheng Gao, Haoxiang Li, Jiaqi Chen, Sijie Li, Fanhong Wei, Lei Zhang, Xuegong |
author_sort | Chen, Yixin |
collection | PubMed |
description | Human Ensemble Cell Atlas (hECA) provides a unified informatics framework and the cell-centric-assembled single-cell transcriptome data of 1,093,299 labeled human cells from 116 published datasets. In this protocol, we provide three applications of hECA: “quantitative portraiture” exploration with websites, customizable reference creation for automatic cell type annotation, and “in data” cell sorting with logical conditions. We provide detail steps of connecting to the database, searching cell with conditions, downloading data, and annotating new datasets with customized reference. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022). |
format | Online Article Text |
id | pubmed-9356166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-93561662022-08-07 Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA Chen, Yixin Hao, Minsheng Gao, Haoxiang Li, Jiaqi Chen, Sijie Li, Fanhong Wei, Lei Zhang, Xuegong STAR Protoc Protocol Human Ensemble Cell Atlas (hECA) provides a unified informatics framework and the cell-centric-assembled single-cell transcriptome data of 1,093,299 labeled human cells from 116 published datasets. In this protocol, we provide three applications of hECA: “quantitative portraiture” exploration with websites, customizable reference creation for automatic cell type annotation, and “in data” cell sorting with logical conditions. We provide detail steps of connecting to the database, searching cell with conditions, downloading data, and annotating new datasets with customized reference. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022). Elsevier 2022-07-31 /pmc/articles/PMC9356166/ /pubmed/35942342 http://dx.doi.org/10.1016/j.xpro.2022.101589 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Chen, Yixin Hao, Minsheng Gao, Haoxiang Li, Jiaqi Chen, Sijie Li, Fanhong Wei, Lei Zhang, Xuegong Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA |
title | Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA |
title_full | Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA |
title_fullStr | Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA |
title_full_unstemmed | Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA |
title_short | Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA |
title_sort | protocol for profiling cell-centric assembled single-cell human transcriptome data in heca |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356166/ https://www.ncbi.nlm.nih.gov/pubmed/35942342 http://dx.doi.org/10.1016/j.xpro.2022.101589 |
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