Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Ying, Chen, Ziyi, Han, Na, Shang, Jingzhe, Wu, Aiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
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
_version_ 1785084272657301504
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
work_keys_str_mv AT jiangying scimmucchierarchicalannotationforimmunecelltypesinsinglecellrnaseq
AT chenziyi scimmucchierarchicalannotationforimmunecelltypesinsinglecellrnaseq
AT hanna scimmucchierarchicalannotationforimmunecelltypesinsinglecellrnaseq
AT shangjingzhe scimmucchierarchicalannotationforimmunecelltypesinsinglecellrnaseq
AT wuaiping scimmucchierarchicalannotationforimmunecelltypesinsinglecellrnaseq