Plate IV.153 · Publications
longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types
Article in Bioinformatics, 14 March 2024.
Abstract
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/.
Cite
@article{hagenberg2024longmixr,
title = {{longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types}},
author = {Hagenberg, Jonas and Budde, Monika and Pandeva, Teodora and Kondofersky, Ivan and Schaupp, Sabrina K and Theis, Fabian J and Schulze, Thomas G and Müller, Nikola S and Heilbronner, Urs and Batra, Richa and Knauer-Arloth, Janine},
journal = {Bioinformatics},
publisher = {Oxford University Press (OUP)},
year = {2024},
month = mar,
doi = {10.1093/bioinformatics/btae137},
url = {https://doi.org/10.1093/bioinformatics/btae137},
pmid = {38485697},
pmcid = {PMC10994717}
}