A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data

Many clustering methods have been proposed, but most of them cannot work for longitudinal gene expression data. 'BClustLonG' is a package that allows us to perform clustering analysis for longitudinal gene expression data. It adopts a linear-mixed effects framework to model the trajectory of genes over time, while clustering is jointly conducted based on the regression coefficients obtained from all genes. To account for the correlations among genes and alleviate the high dimensionality challenges, factor analysis models are adopted for the regression coefficients. The Dirichlet process prior distribution is utilized for the means of the regression coefficients to induce clustering. This package allows users to specify which variables to use for clustering (intercepts or slopes or both) and whether a factor analysis model is desired. More details about this method can be found in Jiehuan Sun, et al. (2017) .


Reference manual

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0.1.3 by Jiehuan Sun, a year ago

Browse source code at https://github.com/cran/BClustLonG

Authors: Jiehuan Sun [aut, cre] , Jose D. Herazo-Maya[aut] , Naftali Kaminski[aut] , Hongyu Zhao [aut] , and Joshua L. Warren [aut] ,

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp

Depends on MASS, lme4, mcclust

Suggests knitr, lattice

Linking to Rcpp, RcppArmadillo

See at CRAN