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A gene profiling deconvolution approach to estimating immune cell composition from complex tissues
BACKGROUND: A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohis...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998872/ https://www.ncbi.nlm.nih.gov/pubmed/29745829 http://dx.doi.org/10.1186/s12859-018-2069-6 |
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author | Chen, Shu-Hwa Kuo, Wen-Yu Su, Sheng-Yao Chung, Wei-Chun Ho, Jen-Ming Lu, Henry Horng-Shing Lin, Chung-Yen |
author_facet | Chen, Shu-Hwa Kuo, Wen-Yu Su, Sheng-Yao Chung, Wei-Chun Ho, Jen-Ming Lu, Henry Horng-Shing Lin, Chung-Yen |
author_sort | Chen, Shu-Hwa |
collection | PubMed |
description | BACKGROUND: A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. RESULTS: Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v -Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. CONCLUSIONS: Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a. |
format | Online Article Text |
id | pubmed-5998872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59988722018-06-25 A gene profiling deconvolution approach to estimating immune cell composition from complex tissues Chen, Shu-Hwa Kuo, Wen-Yu Su, Sheng-Yao Chung, Wei-Chun Ho, Jen-Ming Lu, Henry Horng-Shing Lin, Chung-Yen BMC Bioinformatics Research BACKGROUND: A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. RESULTS: Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v -Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. CONCLUSIONS: Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a. BioMed Central 2018-05-08 /pmc/articles/PMC5998872/ /pubmed/29745829 http://dx.doi.org/10.1186/s12859-018-2069-6 Text en © The Author(s). 2018 Open AccessThis 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 Chen, Shu-Hwa Kuo, Wen-Yu Su, Sheng-Yao Chung, Wei-Chun Ho, Jen-Ming Lu, Henry Horng-Shing Lin, Chung-Yen A gene profiling deconvolution approach to estimating immune cell composition from complex tissues |
title | A gene profiling deconvolution approach to estimating immune cell composition from complex tissues |
title_full | A gene profiling deconvolution approach to estimating immune cell composition from complex tissues |
title_fullStr | A gene profiling deconvolution approach to estimating immune cell composition from complex tissues |
title_full_unstemmed | A gene profiling deconvolution approach to estimating immune cell composition from complex tissues |
title_short | A gene profiling deconvolution approach to estimating immune cell composition from complex tissues |
title_sort | gene profiling deconvolution approach to estimating immune cell composition from complex tissues |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998872/ https://www.ncbi.nlm.nih.gov/pubmed/29745829 http://dx.doi.org/10.1186/s12859-018-2069-6 |
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