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CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation

Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors, such as batch effects and biologically relevant variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical acr...

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Detalles Bibliográficos
Autores principales: Hu, Zhiyuan, Ahmed, Ahmed A., Yau, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667531/
https://www.ncbi.nlm.nih.gov/pubmed/34903266
http://dx.doi.org/10.1186/s13059-021-02561-2
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author Hu, Zhiyuan
Ahmed, Ahmed A.
Yau, Christopher
author_facet Hu, Zhiyuan
Ahmed, Ahmed A.
Yau, Christopher
author_sort Hu, Zhiyuan
collection PubMed
description Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors, such as batch effects and biologically relevant variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across samples. Here, we present CIDER, a meta-clustering workflow based on inter-group similarity measures. We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration approaches in both simulated and real datasets. Moreover, we show that CIDER can be used to assess the biological correctness of integration in real datasets, while it does not require the existence of prior cellular annotations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02561-2.
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spelling pubmed-86675312021-12-14 CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation Hu, Zhiyuan Ahmed, Ahmed A. Yau, Christopher Genome Biol Method Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors, such as batch effects and biologically relevant variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across samples. Here, we present CIDER, a meta-clustering workflow based on inter-group similarity measures. We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration approaches in both simulated and real datasets. Moreover, we show that CIDER can be used to assess the biological correctness of integration in real datasets, while it does not require the existence of prior cellular annotations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02561-2. BioMed Central 2021-12-13 /pmc/articles/PMC8667531/ /pubmed/34903266 http://dx.doi.org/10.1186/s13059-021-02561-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Hu, Zhiyuan
Ahmed, Ahmed A.
Yau, Christopher
CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation
title CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation
title_full CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation
title_fullStr CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation
title_full_unstemmed CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation
title_short CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation
title_sort cider: an interpretable meta-clustering framework for single-cell rna-seq data integration and evaluation
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667531/
https://www.ncbi.nlm.nih.gov/pubmed/34903266
http://dx.doi.org/10.1186/s13059-021-02561-2
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