| Title: | A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data |
|---|---|
| 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) <doi:10.1002/sim.7374>. |
| Authors: | Jiehuan Sun [aut, cre], Jose D. Herazo-Maya[aut], Naftali Kaminski[aut], Hongyu Zhao [aut], and Joshua L. Warren [aut], |
| Maintainer: | Jiehuan Sun <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1.3 |
| Built: | 2024-10-01 06:44:00 UTC |
| Source: | CRAN |
A Dirichlet process mixture model for clustering longitudinal gene expression data.
BClustLonG( data = NULL, iter = 20000, thin = 2, savePara = FALSE, infoVar = c("both", "int")[1], factor = TRUE, hyperPara = list(v1 = 0.1, v2 = 0.1, v = 1.5, c = 1, a = 0, b = 10, cd = 1, aa1 = 2, aa2 = 1, alpha0 = -1, alpha1 = -1e-04, cutoff = 1e-04, h = 100) )BClustLonG( data = NULL, iter = 20000, thin = 2, savePara = FALSE, infoVar = c("both", "int")[1], factor = TRUE, hyperPara = list(v1 = 0.1, v2 = 0.1, v = 1.5, c = 1, a = 0, b = 10, cd = 1, aa1 = 2, aa2 = 1, alpha0 = -1, alpha1 = -1e-04, cutoff = 1e-04, h = 100) )
data |
Data list with three elements: Y (gene expression data with each column being one gene), ID, and years. (The names of the elements have to be matached exactly. See the data in the example section more info) |
iter |
Number of iterations (excluding the thinning). |
thin |
Number of thinnings. |
savePara |
Logical variable indicating if all the parameters needed to be saved. Default value is FALSE, in which case only the membership indicators are saved. |
infoVar |
Either "both" (using both intercepts and slopes for clustering) or "int" (using only intercepts for clustering) |
factor |
Logical variable indicating whether factor analysis model is wanted. |
hyperPara |
A list of hyperparameters with default values. |
returns a list with following objects.
e.mat |
Membership indicators from all iterations. |
All other parameters |
only returned when savePara=TRUE. |
Jiehuan Sun, Jose D. Herazo-Maya, Naftali Kaminski, Hongyu Zhao, and Joshua L. Warren. "A Dirichlet process mixture model for clustering longitudinal gene expression data." Statistics in Medicine 36, No. 22 (2017): 3495-3506.
data(data) ## increase the number of iterations ## to ensure convergence of the algorithm res = BClustLonG(data, iter=20, thin=2,savePara=FALSE, infoVar="both",factor=TRUE) ## discard the first 10 burn-ins in the e.mat ## and calculate similarity matrix ## the number of burn-ins has be chosen s.t. the algorithm is converged. mat = calSim(t(res$e.mat[,11:20])) clust = maxpear(mat)$cl ## the clustering results. ## Not run: ## if only want to include intercepts for clustering ## set infoVar="int" res = BClustLonG(data, iter=10, thin=2,savePara=FALSE, infoVar="int",factor=TRUE) ## if no factor analysis model is wanted ## set factor=FALSE res = BClustLonG(data, iter=10, thin=2,savePara=FALSE, infoVar="int",factor=TRUE) ## End(Not run)data(data) ## increase the number of iterations ## to ensure convergence of the algorithm res = BClustLonG(data, iter=20, thin=2,savePara=FALSE, infoVar="both",factor=TRUE) ## discard the first 10 burn-ins in the e.mat ## and calculate similarity matrix ## the number of burn-ins has be chosen s.t. the algorithm is converged. mat = calSim(t(res$e.mat[,11:20])) clust = maxpear(mat)$cl ## the clustering results. ## Not run: ## if only want to include intercepts for clustering ## set infoVar="int" res = BClustLonG(data, iter=10, thin=2,savePara=FALSE, infoVar="int",factor=TRUE) ## if no factor analysis model is wanted ## set factor=FALSE res = BClustLonG(data, iter=10, thin=2,savePara=FALSE, infoVar="int",factor=TRUE) ## End(Not run)
Function to calculate the similarity matrix based on the cluster membership indicator of each iteration.
calSim(mat)calSim(mat)
mat |
Matrix of cluster membership indicator from all iterations |
n = 90 ##number of subjects iters = 200 ##number of iterations ## matrix of cluster membership indicators ## perfect clustering with three clusters mat = matrix(rep(1:3,each=n/3),nrow=n,ncol=iters) sim = calSim(t(mat))n = 90 ##number of subjects iters = 200 ##number of iterations ## matrix of cluster membership indicators ## perfect clustering with three clusters mat = matrix(rep(1:3,each=n/3),nrow=n,ncol=iters) sim = calSim(t(mat))
Simulated dataset for testing the algorithm
data(data)data(data)
An object of class list of length 3.
data(data) ## this is the required data input format head(data.frame(ID=data$ID,years=data$years,data$Y))data(data) ## this is the required data input format head(data.frame(ID=data$ID,years=data$years,data$Y))