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Annotating cell types in human single-cell RNA-seq data with CellO
Cell type annotation is important in the analysis of single-cell RNA-seq data. CellO is a machine-learning-based tool for annotating cells using the Cell Ontology, a rich hierarchy of known cell types. We provide a protocol for using the CellO Python package to annotate human cells. We demonstrate h...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379521/ https://www.ncbi.nlm.nih.gov/pubmed/34458864 http://dx.doi.org/10.1016/j.xpro.2021.100705 |
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author | Bernstein, Matthew N. Dewey, Colin N. |
author_facet | Bernstein, Matthew N. Dewey, Colin N. |
author_sort | Bernstein, Matthew N. |
collection | PubMed |
description | Cell type annotation is important in the analysis of single-cell RNA-seq data. CellO is a machine-learning-based tool for annotating cells using the Cell Ontology, a rich hierarchy of known cell types. We provide a protocol for using the CellO Python package to annotate human cells. We demonstrate how to use CellO in conjunction with Scanpy, a Python library for performing single-cell analysis, annotate a lung tissue data set, interpret its hierarchically structured cell type annotations, and create publication-ready figures. For complete details on the use and execution of this protocol, please refer to Bernstein et al. (2021). |
format | Online Article Text |
id | pubmed-8379521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-83795212021-08-27 Annotating cell types in human single-cell RNA-seq data with CellO Bernstein, Matthew N. Dewey, Colin N. STAR Protoc Protocol Cell type annotation is important in the analysis of single-cell RNA-seq data. CellO is a machine-learning-based tool for annotating cells using the Cell Ontology, a rich hierarchy of known cell types. We provide a protocol for using the CellO Python package to annotate human cells. We demonstrate how to use CellO in conjunction with Scanpy, a Python library for performing single-cell analysis, annotate a lung tissue data set, interpret its hierarchically structured cell type annotations, and create publication-ready figures. For complete details on the use and execution of this protocol, please refer to Bernstein et al. (2021). Elsevier 2021-08-17 /pmc/articles/PMC8379521/ /pubmed/34458864 http://dx.doi.org/10.1016/j.xpro.2021.100705 Text en © 2021 The Authors 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 Bernstein, Matthew N. Dewey, Colin N. Annotating cell types in human single-cell RNA-seq data with CellO |
title | Annotating cell types in human single-cell RNA-seq data with CellO |
title_full | Annotating cell types in human single-cell RNA-seq data with CellO |
title_fullStr | Annotating cell types in human single-cell RNA-seq data with CellO |
title_full_unstemmed | Annotating cell types in human single-cell RNA-seq data with CellO |
title_short | Annotating cell types in human single-cell RNA-seq data with CellO |
title_sort | annotating cell types in human single-cell rna-seq data with cello |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379521/ https://www.ncbi.nlm.nih.gov/pubmed/34458864 http://dx.doi.org/10.1016/j.xpro.2021.100705 |
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