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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...

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Detalles Bibliográficos
Autores principales: Bernstein, Matthew N., Dewey, Colin N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
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).
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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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