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Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools

Single-cell RNA sequencing data require several processing procedures to arrive at interpretable results. While commercial platforms can serve as “one-stop shops” for data analysis, they relinquish the flexibility required for customized analyses and are often inflexible between experimental systems...

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Detalles Bibliográficos
Autores principales: Chen, Bob, Ramirez-Solano, Marisol A., Heiser, Cody N., Liu, Qi, Lau, Ken S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082116/
https://www.ncbi.nlm.nih.gov/pubmed/33982010
http://dx.doi.org/10.1016/j.xpro.2021.100450
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author Chen, Bob
Ramirez-Solano, Marisol A.
Heiser, Cody N.
Liu, Qi
Lau, Ken S.
author_facet Chen, Bob
Ramirez-Solano, Marisol A.
Heiser, Cody N.
Liu, Qi
Lau, Ken S.
author_sort Chen, Bob
collection PubMed
description Single-cell RNA sequencing data require several processing procedures to arrive at interpretable results. While commercial platforms can serve as “one-stop shops” for data analysis, they relinquish the flexibility required for customized analyses and are often inflexible between experimental systems. For instance, there is no universal solution for the discrimination of informative or uninformative encapsulated cellular material; thus, pipeline flexibility takes priority. Here, we demonstrate a full data analysis pipeline, constructed modularly from open-source software, including tools that we have contributed. For complete details on the use and execution of this protocol, please refer to Petukhov et al. (2018), Heiser et al. (2020), and Heiser and Lau (2020).
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spelling pubmed-80821162021-05-11 Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools Chen, Bob Ramirez-Solano, Marisol A. Heiser, Cody N. Liu, Qi Lau, Ken S. STAR Protoc Protocol Single-cell RNA sequencing data require several processing procedures to arrive at interpretable results. While commercial platforms can serve as “one-stop shops” for data analysis, they relinquish the flexibility required for customized analyses and are often inflexible between experimental systems. For instance, there is no universal solution for the discrimination of informative or uninformative encapsulated cellular material; thus, pipeline flexibility takes priority. Here, we demonstrate a full data analysis pipeline, constructed modularly from open-source software, including tools that we have contributed. For complete details on the use and execution of this protocol, please refer to Petukhov et al. (2018), Heiser et al. (2020), and Heiser and Lau (2020). Elsevier 2021-04-17 /pmc/articles/PMC8082116/ /pubmed/33982010 http://dx.doi.org/10.1016/j.xpro.2021.100450 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Chen, Bob
Ramirez-Solano, Marisol A.
Heiser, Cody N.
Liu, Qi
Lau, Ken S.
Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools
title Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools
title_full Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools
title_fullStr Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools
title_full_unstemmed Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools
title_short Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools
title_sort processing single-cell rna-seq data for dimension reduction-based analyses using open-source tools
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082116/
https://www.ncbi.nlm.nih.gov/pubmed/33982010
http://dx.doi.org/10.1016/j.xpro.2021.100450
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