Description Details Author(s) References See Also Examples

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 `tc.data`

.

This package three sets of functions.

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.

Package: | DIRECT |

Type: | Package |

Version: | 1.0 |

Date: | 2011-10-19 |

License: | GPL (>=2) |

LazyLoad: | yes |

See `DIRECT`

for details on using the function for clustering.

See `summaryDIRECT`

, which points to other related plotting functions, for details on how to process MCMC samples and display clustering results.

See `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.

1 | ```
## See examples in DIRECT and simuDataREM.
``` |

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