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)
|Author||Jiehuan Sun [aut, cre], Jose D. Herazo-Maya[aut], Naftali Kaminski[aut], Hongyu Zhao [aut], and Joshua L. Warren [aut],|
|Date of publication||2017-12-15 13:52:52 UTC|
|Maintainer||Jiehuan Sun <[email protected]>|
|Package repository||View on CRAN|
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