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MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster...

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Autores principales: Liu, Siyao, Thennavan, Aatish, Garay, Joseph P., Marron, J. S., Perou, Charles M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375188/
https://www.ncbi.nlm.nih.gov/pubmed/34412669
http://dx.doi.org/10.1186/s13059-021-02445-5
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author Liu, Siyao
Thennavan, Aatish
Garay, Joseph P.
Marron, J. S.
Perou, Charles M.
author_facet Liu, Siyao
Thennavan, Aatish
Garay, Joseph P.
Marron, J. S.
Perou, Charles M.
author_sort Liu, Siyao
collection PubMed
description Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02445-5.
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spelling pubmed-83751882021-08-23 MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data Liu, Siyao Thennavan, Aatish Garay, Joseph P. Marron, J. S. Perou, Charles M. Genome Biol Method Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02445-5. BioMed Central 2021-08-19 /pmc/articles/PMC8375188/ /pubmed/34412669 http://dx.doi.org/10.1186/s13059-021-02445-5 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
Liu, Siyao
Thennavan, Aatish
Garay, Joseph P.
Marron, J. S.
Perou, Charles M.
MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data
title MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data
title_full MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data
title_fullStr MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data
title_full_unstemmed MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data
title_short MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data
title_sort multik: an automated tool to determine optimal cluster numbers in single-cell rna sequencing data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375188/
https://www.ncbi.nlm.nih.gov/pubmed/34412669
http://dx.doi.org/10.1186/s13059-021-02445-5
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