BClustLonG
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) doi:10.1002/sim.7374.
- Version0.1.3
- R version≥ 3.4.0
- LicenseGPL-2
- Needs compilation?Yes
- Last release05/07/2020
Documentation
Team
Jiehuan Sun
Joshua L. Warren
Show author detailsRolesAuthorHongyu Zhao
Show author detailsRolesAuthorJose D. Herazo-Maya
Show author detailsRolesAuthorNaftali Kaminski
Show author detailsRolesAuthor
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Last 30 days
This package has been downloaded 212 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 16 times.
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Last 365 days
This package has been downloaded 2,613 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 28 downloads.
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Dependencies
- Depends3 packages
- Imports1 package
- Suggests2 packages
- Linking To2 packages