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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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Domain Criteria Rdoc; R-package; Class Discovery
ISSN (print) / ISBN 1367-4803
Zeitschrift Bioinformatics
Quellenangaben Band: 40, Heft: 4, Seiten: , Artikelnummer: btae137 Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen 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