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Estimation of immune cell content in tumor using single-cell RNA-seq reference data
BACKGROUND: The rapid development of single-cell RNA sequencing (scRNA-seq) provides unprecedented opportunities to study the tumor ecosystem that involves a heterogeneous mixture of cell types. However, the majority of previous and current studies related to translational and molecular oncology hav...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642583/ https://www.ncbi.nlm.nih.gov/pubmed/31324168 http://dx.doi.org/10.1186/s12885-019-5927-3 |
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author | Yu, Xiaoqing Chen, Y. Ann Conejo-Garcia, Jose R. Chung, Christine H. Wang, Xuefeng |
author_facet | Yu, Xiaoqing Chen, Y. Ann Conejo-Garcia, Jose R. Chung, Christine H. Wang, Xuefeng |
author_sort | Yu, Xiaoqing |
collection | PubMed |
description | BACKGROUND: The rapid development of single-cell RNA sequencing (scRNA-seq) provides unprecedented opportunities to study the tumor ecosystem that involves a heterogeneous mixture of cell types. However, the majority of previous and current studies related to translational and molecular oncology have only focused on the bulk tumor and there is a wealth of gene expression data accumulated with matched clinical outcomes. RESULTS: In this paper, we introduce a scheme for characterizing cell compositions from bulk tumor gene expression by integrating signatures learned from scRNA-seq data. We derived the reference expression matrix to each cell type based on cell subpopulations identified in head and neck cancer dataset. Our results suggest that scRNA-Seq-derived reference matrix outperforms the existing gene panel and reference matrix with respect to distinguishing immune cell subtypes. CONCLUSIONS: Findings and resources created from this study enable future and secondary analysis of tumor RNA mixtures in head and neck cancer for a more accurate cellular deconvolution, and can facilitate the profiling of the immune infiltration in other solid tumors due to the expression homogeneity observed in immune cells. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-019-5927-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6642583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66425832019-07-29 Estimation of immune cell content in tumor using single-cell RNA-seq reference data Yu, Xiaoqing Chen, Y. Ann Conejo-Garcia, Jose R. Chung, Christine H. Wang, Xuefeng BMC Cancer Research Article BACKGROUND: The rapid development of single-cell RNA sequencing (scRNA-seq) provides unprecedented opportunities to study the tumor ecosystem that involves a heterogeneous mixture of cell types. However, the majority of previous and current studies related to translational and molecular oncology have only focused on the bulk tumor and there is a wealth of gene expression data accumulated with matched clinical outcomes. RESULTS: In this paper, we introduce a scheme for characterizing cell compositions from bulk tumor gene expression by integrating signatures learned from scRNA-seq data. We derived the reference expression matrix to each cell type based on cell subpopulations identified in head and neck cancer dataset. Our results suggest that scRNA-Seq-derived reference matrix outperforms the existing gene panel and reference matrix with respect to distinguishing immune cell subtypes. CONCLUSIONS: Findings and resources created from this study enable future and secondary analysis of tumor RNA mixtures in head and neck cancer for a more accurate cellular deconvolution, and can facilitate the profiling of the immune infiltration in other solid tumors due to the expression homogeneity observed in immune cells. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-019-5927-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-19 /pmc/articles/PMC6642583/ /pubmed/31324168 http://dx.doi.org/10.1186/s12885-019-5927-3 Text en © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Yu, Xiaoqing Chen, Y. Ann Conejo-Garcia, Jose R. Chung, Christine H. Wang, Xuefeng Estimation of immune cell content in tumor using single-cell RNA-seq reference data |
title | Estimation of immune cell content in tumor using single-cell RNA-seq reference data |
title_full | Estimation of immune cell content in tumor using single-cell RNA-seq reference data |
title_fullStr | Estimation of immune cell content in tumor using single-cell RNA-seq reference data |
title_full_unstemmed | Estimation of immune cell content in tumor using single-cell RNA-seq reference data |
title_short | Estimation of immune cell content in tumor using single-cell RNA-seq reference data |
title_sort | estimation of immune cell content in tumor using single-cell rna-seq reference data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642583/ https://www.ncbi.nlm.nih.gov/pubmed/31324168 http://dx.doi.org/10.1186/s12885-019-5927-3 |
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