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Hagenberg, J. ; Budde, M.* ; Pandeva, T. ; Kondofersky, I. ; Schaupp, S.K.* ; Theis, F.J. ; Schulze, T.G.* ; Müller, N.S. ; Heilbronner, U.* ; Batra, R. ; Knauer-Arloth, J.

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

Bioinformatics 40:btae137 (2024)
DOI PMC
Creative Commons Lizenzvertrag
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/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Domain Criteria Rdoc; R-package; Class Discovery
ISSN (print) / ISBN 1367-4803
Journal Bioinformatics
Quellenangaben Volume: 40, Issue: 4, Pages: , Article Number: btae137 Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants NARSAD Young Investigator Grant
Deutsche Forschungsgemeinschaft
Dr Lisa Oehler Foundation (Kassel, Germany)
Bundesministerium fur Bildung und Forschung (BMBF, Federal Ministry of Education and Research)
National Institute of Aging of the National Institutes of Health
Alzheimer's association award
European Union