This package implements the Bayesian clustering method in Fu et al. (2013). It also contains a simulation function to generate data under the random-effects mixture model presented in this paper, as well as summary and plotting functions to process MCMC samples and display the clustering results. Replicated time-course microarray gene expression data analyzed in this paper are in
This package three sets of functions.
DIRECT and others for clustering data. They estimate the number of clusters and infers the partition for multivariate data, e.g., replicated time-course microarray gene expression data. The clustering method involves a random-effects mixture model that decomposes the total variability in the data into within-cluster variability, variability across experimental conditions (e.g., time points), and variability in replicates (i.e., residual variability). The clustering method uses a Dirichlet-process prior to induce a distribution on the number of clusters as well as clustering. It uses Metropolis-Hastings Markov chain Monte Carlo for parameter estimation. To estimate the posterior allocation probability matrix while dealing with the label-switching problem, there is a two-step posterior inference procedure involving resampling and relabeling.
Functions for processing MCMC samples and plotting the clustering results.
Functions for simulating data under the random-effects mixture model.
DIRECT for details on using the function for clustering.
summaryDIRECT, which points to other related plotting functions, for details on how to process MCMC samples and display clustering results.
simuDataREM, which points to other related functions, for simulating data under the random-effects mixture model.
Audrey Qiuyan Fu
Maintainer: Audrey Q. Fu <[email protected]>
Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361.
DIRECT for the clustering method.
summaryDIRECT for processing MCMC estimates for clustering.
simuDataREM for simulating data under the mixture random-effects model.
## See examples in DIRECT and simuDataREM.
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