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Identification of relevant subtypes via preweighted sparse clustering

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  • Gaynor, Sheila
  • Bair, Eric

Abstract

Cluster analysis methods are used to identify homogeneous subgroups in a data set. In biomedical applications, one frequently applies cluster analysis in order to identify biologically interesting subgroups. In particular, one may wish to identify subgroups that are associated with a particular outcome of interest. Conventional clustering methods generally do not identify such subgroups, particularly when there are a large number of high-variance features in the data set. Conventional methods may identify clusters associated with these high-variance features when one wishes to obtain secondary clusters that are more interesting biologically or more strongly associated with a particular outcome of interest. A modification of sparse clustering can be used to identify such secondary clusters or clusters associated with an outcome of interest. This method correctly identifies such clusters of interest in several simulation scenarios. The method is also applied to a large prospective cohort study of temporomandibular disorders and a leukemia microarray data set.

Suggested Citation

  • Gaynor, Sheila & Bair, Eric, 2017. "Identification of relevant subtypes via preweighted sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 139-154.
    • Handle: RePEc:eee:csdana:v:116:y:2017:i:c:p:139-154
    DOI: 10.1016/j.csda.2017.06.003
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    References listed on IDEAS

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    Cited by:

    1. Lingsong Meng & Dorina Avram & George Tseng & Zhiguang Huo, 2022. "Outcome‐guided sparse K‐means for disease subtype discovery via integrating phenotypic data with high‐dimensional transcriptomic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 352-375, March.
    2. Erika S. Helgeson & David M. Vock & Eric Bair, 2021. "Nonparametric cluster significance testing with reference to a unimodal null distribution," Biometrics, The International Biometric Society, vol. 77(4), pages 1215-1226, December.

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