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Journal Article

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

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Hagenberg,  Jonas
Dept. Genes and Environment, Max Planck Institute of Psychiatry, Max Planck Society;
IMPRS Translational Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

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Knauer-Arloth,  Janine
Dept. Genes and Environment, Max Planck Institute of Psychiatry, Max Planck Society;

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Citation

Hagenberg, J., Budde, M., Pandeva, T., Kondofersky, I., Theis, F. J., Schaupp, S. K., et al. (2024). longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types. BIOINFORMATICS, 40(4): btae137. doi:10.1093/bioinformatics/btae137.


Cite as: https://hdl.handle.net/21.11116/0000-000F-2E43-3
Abstract
The 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/.