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Prediction of distinct populations of innate lymphoid cells by transcriptional profiles
Innate lymphoid cells (ILCs) are a unique type of lymphocyte that differ from adaptive lymphocytes in that they lack antigen receptors, which primarily reside in tissues and are closely associated with fibers. Despite their plasticity and heterogeneity, identifying ILCs in peripheral blood can be di...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500302/ https://www.ncbi.nlm.nih.gov/pubmed/37719706 http://dx.doi.org/10.3389/fgene.2023.1227452 |
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author | Dong, Haiyao Du, Zhenguang Ma, Haoming Zhou, Zhicheng Yang, Haitao Wang, Zhenyuan |
author_facet | Dong, Haiyao Du, Zhenguang Ma, Haoming Zhou, Zhicheng Yang, Haitao Wang, Zhenyuan |
author_sort | Dong, Haiyao |
collection | PubMed |
description | Innate lymphoid cells (ILCs) are a unique type of lymphocyte that differ from adaptive lymphocytes in that they lack antigen receptors, which primarily reside in tissues and are closely associated with fibers. Despite their plasticity and heterogeneity, identifying ILCs in peripheral blood can be difficult due to their small numbers. Accurately and rapidly identifying ILCs is critical for studying homeostasis and inflammation. To address this challenge, we collect single-cell RNA-seq data from 647 patients, including 26,087 transcripts. Background screening, Lasso analysis, and principal component analysis (PCA) are used to select features. Finally, we employ a deep neural network to classify lymphocytes. Our method achieved the highest accuracy compared to other approaches. Furthermore, we identified four genes that play a vital role in lymphocyte development. Adding these gene transcripts into model, we were able to increase the model’s AUC. In summary, our study demonstrates the effectiveness of using single-cell transcriptomic analysis combined with machine learning techniques to accurately identify congenital lymphoid cells and advance our understanding of their development and function in the body. |
format | Online Article Text |
id | pubmed-10500302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105003022023-09-15 Prediction of distinct populations of innate lymphoid cells by transcriptional profiles Dong, Haiyao Du, Zhenguang Ma, Haoming Zhou, Zhicheng Yang, Haitao Wang, Zhenyuan Front Genet Genetics Innate lymphoid cells (ILCs) are a unique type of lymphocyte that differ from adaptive lymphocytes in that they lack antigen receptors, which primarily reside in tissues and are closely associated with fibers. Despite their plasticity and heterogeneity, identifying ILCs in peripheral blood can be difficult due to their small numbers. Accurately and rapidly identifying ILCs is critical for studying homeostasis and inflammation. To address this challenge, we collect single-cell RNA-seq data from 647 patients, including 26,087 transcripts. Background screening, Lasso analysis, and principal component analysis (PCA) are used to select features. Finally, we employ a deep neural network to classify lymphocytes. Our method achieved the highest accuracy compared to other approaches. Furthermore, we identified four genes that play a vital role in lymphocyte development. Adding these gene transcripts into model, we were able to increase the model’s AUC. In summary, our study demonstrates the effectiveness of using single-cell transcriptomic analysis combined with machine learning techniques to accurately identify congenital lymphoid cells and advance our understanding of their development and function in the body. Frontiers Media S.A. 2023-08-31 /pmc/articles/PMC10500302/ /pubmed/37719706 http://dx.doi.org/10.3389/fgene.2023.1227452 Text en Copyright © 2023 Dong, Du, Ma, Zhou, Yang and Wang. 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 | Genetics Dong, Haiyao Du, Zhenguang Ma, Haoming Zhou, Zhicheng Yang, Haitao Wang, Zhenyuan Prediction of distinct populations of innate lymphoid cells by transcriptional profiles |
title | Prediction of distinct populations of innate lymphoid cells by transcriptional profiles |
title_full | Prediction of distinct populations of innate lymphoid cells by transcriptional profiles |
title_fullStr | Prediction of distinct populations of innate lymphoid cells by transcriptional profiles |
title_full_unstemmed | Prediction of distinct populations of innate lymphoid cells by transcriptional profiles |
title_short | Prediction of distinct populations of innate lymphoid cells by transcriptional profiles |
title_sort | prediction of distinct populations of innate lymphoid cells by transcriptional profiles |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500302/ https://www.ncbi.nlm.nih.gov/pubmed/37719706 http://dx.doi.org/10.3389/fgene.2023.1227452 |
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