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...

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Detalles Bibliográficos
Autores principales: Okada, Daigo, Cheng, Jian Hao, Zheng, Cheng, Yamada, Ryo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2021
Materias:
Article
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.
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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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