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.

Open accessResearch area: Independent Component, Graph

Authors

  1. Jonas Hagenberg
  2. Monika Budde
  3. Teodora Pandeva
  4. Ivan Kondofersky
  5. Sabrina K Schaupp
  6. Fabian J Theis
  7. Thomas G Schulze
  8. Nikola S Müller
  9. Urs Heilbronner
  10. Richa Batra
  11. Janine Knauer-Arloth

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}
}
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