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Data-driven comparison of multiple high-dimensional single-cell expression profiles
Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of con...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Singapore
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8948086/ https://www.ncbi.nlm.nih.gov/pubmed/34719682 http://dx.doi.org/10.1038/s10038-021-00989-9 |
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author | Okada, Daigo Cheng, Jian Hao Zheng, Cheng Yamada, Ryo |
author_facet | Okada, Daigo Cheng, Jian Hao Zheng, Cheng Yamada, Ryo |
author_sort | Okada, Daigo |
collection | PubMed |
description | Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets. |
format | Online Article Text |
id | pubmed-8948086 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-89480862022-04-07 Data-driven comparison of multiple high-dimensional single-cell expression profiles Okada, Daigo Cheng, Jian Hao Zheng, Cheng Yamada, Ryo J Hum Genet Article Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets. Springer Singapore 2021-11-01 2022 /pmc/articles/PMC8948086/ /pubmed/34719682 http://dx.doi.org/10.1038/s10038-021-00989-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Okada, Daigo Cheng, Jian Hao Zheng, Cheng Yamada, Ryo Data-driven comparison of multiple high-dimensional single-cell expression profiles |
title | Data-driven comparison of multiple high-dimensional single-cell expression profiles |
title_full | Data-driven comparison of multiple high-dimensional single-cell expression profiles |
title_fullStr | Data-driven comparison of multiple high-dimensional single-cell expression profiles |
title_full_unstemmed | Data-driven comparison of multiple high-dimensional single-cell expression profiles |
title_short | Data-driven comparison of multiple high-dimensional single-cell expression profiles |
title_sort | data-driven comparison of multiple high-dimensional single-cell expression profiles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8948086/ https://www.ncbi.nlm.nih.gov/pubmed/34719682 http://dx.doi.org/10.1038/s10038-021-00989-9 |
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