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longmixr:一个用于混合数据类型的高维横截面和纵向变量的稳健聚类的工具。

longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.

机构信息

Max Planck Institute of Psychiatry, 80804 Munich, Germany.

International Max Planck Research School for Translational Psychiatry, 80804 Munich, Germany.

出版信息

Bioinformatics. 2024 Mar 29;40(4). doi: 10.1093/bioinformatics/btae137.

Abstract

SUMMARY

Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization.

AVAILABILITY AND IMPLEMENTATION

The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

摘要

摘要

准确地对混合数据进行聚类,包括二进制、分类和连续变量,对于临床问卷分析中的有效患者分层至关重要。为此,我们提出了 longmixr,这是一个全面的 R 包,提供了使用有限混合模型技术对混合纵向数据进行聚类的强大框架。通过合并一致聚类,longmixr 确保了可靠和稳定的聚类结果。此外,该包还包括一个详细的实例,以方便聚类的探索和可视化。

可用性和实现

该 R 包可在 https://cran.r-project.org/package=longmixr 上免费获取,详细文档包括案例实例,可在 https://cellmapslab.github.io/longmixr/ 获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f5c/10994717/2abd465a41f8/btae137f1.jpg

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