Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data

Baolin Wu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Currently, extreme large-scale genetic data present significant challenges for cluster analysis. Most of the existing clustering methods are typically built on the Euclidean distance and geared toward analyzing continuous response. They work well for clustering, e.g. microarray gene expression data, but often perform poorly for clustering, e.g. large-scale single nucleotide polymorphism (SNP) data. In this paper, we study the penalized latent class model for clustering extremely large-scale discrete data. The penalized latent class model takes into account the discrete nature of the response using appropriate generalized linear models and adopts the lasso penalized likelihood approach for simultaneous model estimation and selection of important covariates. We develop very efficient numerical algorithms for model estimation based on the iterative coordinate descent approach and further develop the expectation-maximization algorithm to incorporate and model missing values. We use simulation studies and applications to the international HapMap SNP data to illustrate the competitive performance of the penalized latent class model.

Original languageEnglish (US)
Pages (from-to)358-367
Number of pages10
JournalJournal of Applied Statistics
Issue number2
StatePublished - Feb 2013

Bibliographical note

Funding Information:
This research was supported in part by NIH grant GM083345 and CA134848. I would like to thank two anonymous referees for their constructive comments that have dramatically improved the presentation of the paper.


  • clustering
  • expectation-maximization algorithm
  • k-means
  • lasso
  • latent class model
  • principal components
  • single nucleotide polymorphism
  • sparse clustering


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