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tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis

SUMMARY: While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized—this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each ste...

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Detalles Bibliográficos
Autores principales: Keyes, Timothy J, Koladiya, Abhishek, Lo, Yu-Chen, Nolan, Garry P, Davis, Kara L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281957/
https://www.ncbi.nlm.nih.gov/pubmed/37351311
http://dx.doi.org/10.1093/bioadv/vbad071
Descripción
Sumario:SUMMARY: While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized—this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular ‘tidy data’ interface. AVAILABILITY AND IMPLEMENTATION: {tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.