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BClustLonG
View on CRAN: Click
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Download and install BClustLonG package within the R console
Install from CRAN:
install.packages("BClustLonG")
Install from Github:
library("remotes")
install_github("cran/BClustLonG")
Install by package version:
library("remotes")
install_version("BClustLonG", "0.1.3")
Attach the package and use:
library("BClustLonG")
Maintained by
Jiehuan Sun
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-12-15
Latest Update: 2020-05-07
Description:
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) .
How to cite:
Jiehuan Sun (2017). BClustLonG: A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data. R package version 0.1.3, https://cran.r-project.org/web/packages/BClustLonG
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Other R packages that BClustLonG depends,
imports, suggests or enhances
Functions, R codes and Examples using
the BClustLonG R package
Some associated functions: BClustLonG . calSim . data .
Some associated R codes: RcppExports.R . sparseFactor.R . Full BClustLonG package functions and examples
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