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

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

Content Provider	Scilit
Author	Wu, Baolin
Copyright Year	2013
Description	Current extremely large scale genetic data presents significant challenges for cluster analysis. Most existing clustering methods are typically built on 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 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 single nucleotide polymorphism data to illustrate the competitive performance of the penalized latent class model.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3601766/pdf
Ending Page	367
Page Count	10
Starting Page	358
ISSN	02664763
e-ISSN	13600532
DOI	10.1080/02664763.2012.743977
Journal	Journal of Applied Statistics
Issue Number	2
Volume Number	40
Language	English
Publisher	Informa UK Limited
Publisher Date	2013-02-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Applied Statistics Statistics and Probability Clustering Expectation-maximization Algorithm Latent Class Model Lasso K-means Principal Components Single Nucleotide Polymorphism Sparse Clustering
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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