tidytof：一个用于可扩展和可重复的高维细胞计数数据分析的用户友好框架。
tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.
作者信息
Keyes Timothy J, Koladiya Abhishek, Lo Yu-Chen, Nolan Garry P, Davis Kara L
机构信息
Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA.
Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA.
出版信息
Bioinform Adv. 2023 Jun 9;3(1):vbad071. doi: 10.1093/bioadv/vbad071. eCollection 2023.
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 online.
摘要
虽然目前已经开发出许多用于分析高维细胞计数数据的算法，但这些算法的软件实现仍然高度定制化——这意味着探索一个数据集需要用户为数据处理的每个步骤学习独特的、通常难以互操作的包语法。为了解决这个问题，我们开发了{tidytof}，这是一个用于使用日益流行的“整洁数据”接口分析高维细胞计数数据的开源R包。
可用性与实现
{tidytof}可在https://github.com/keyes-timothy/tidytof获取，并根据麻省理工学院许可发布。它在Linux、MS Windows和MacOS上均受支持。其他文档可在包网站(https://keyes-timothy.github.io/tidytof/)获取。
补充信息
补充数据可在网上获取。
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