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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) <doi:10.1002/sim.7374>.
| Version: | 0.1.3 |
| Depends: | R (≥ 3.4.0), MASS (≥ 7.3-47), lme4 (≥ 1.1-13), mcclust (≥ 1.0) |
| Imports: | Rcpp (≥ 0.12.7) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, lattice |
| Published: | 2020-05-07 |
| DOI: | 10.32614/CRAN.package.BClustLonG |
| Author: | Jiehuan Sun [aut, cre], Jose D. Herazo-Maya[aut], Naftali Kaminski[aut], Hongyu Zhao [aut], and Joshua L. Warren [aut], |
| Maintainer: | Jiehuan Sun <jiehuan.sun at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| In views: | Omics |
| CRAN checks: | BClustLonG results |
| Reference manual: | BClustLonG.pdf |
| Vignettes: |
BClustLonG |
| Package source: | BClustLonG_0.1.3.tar.gz |
| Windows binaries: | r-devel: BClustLonG_0.1.3.zip, r-release: BClustLonG_0.1.3.zip, r-oldrel: BClustLonG_0.1.3.zip |
| macOS binaries: | r-release (arm64): BClustLonG_0.1.3.tgz, r-oldrel (arm64): BClustLonG_0.1.3.tgz, r-release (x86_64): BClustLonG_0.1.3.tgz, r-oldrel (x86_64): BClustLonG_0.1.3.tgz |
| Old sources: | BClustLonG archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.