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sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq
Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, po...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399579/ https://www.ncbi.nlm.nih.gov/pubmed/37545533 http://dx.doi.org/10.3389/fimmu.2023.1223471 |
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author | Jiang, Ying Chen, Ziyi Han, Na Shang, Jingzhe Wu, Aiping |
author_facet | Jiang, Ying Chen, Ziyi Han, Na Shang, Jingzhe Wu, Aiping |
author_sort | Jiang, Ying |
collection | PubMed |
description | Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions. |
format | Online Article Text |
id | pubmed-10399579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103995792023-08-04 sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq Jiang, Ying Chen, Ziyi Han, Na Shang, Jingzhe Wu, Aiping Front Immunol Immunology Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions. Frontiers Media S.A. 2023-07-20 /pmc/articles/PMC10399579/ /pubmed/37545533 http://dx.doi.org/10.3389/fimmu.2023.1223471 Text en Copyright © 2023 Jiang, Chen, Han, Shang and Wu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Jiang, Ying Chen, Ziyi Han, Na Shang, Jingzhe Wu, Aiping sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq |
title | sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq |
title_full | sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq |
title_fullStr | sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq |
title_full_unstemmed | sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq |
title_short | sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq |
title_sort | sc-immucc: hierarchical annotation for immune cell types in single-cell rna-seq |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399579/ https://www.ncbi.nlm.nih.gov/pubmed/37545533 http://dx.doi.org/10.3389/fimmu.2023.1223471 |
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