This function clusters gene expression by including uncertainties of gene expression measurements from probe-level analysis models and replicate information into a robust t mixture clustering model. The inputs are gene expression levels and the probe-level standard deviation associated with expression measurement for each gene on each chip. The outputs is the clustering results.

1 2 3 |

`e` |
data frame containing the expression level for each gene on each chip. |

`se` |
data frame containing the standard deviation of gene expression levels. |

`efile` |
character, the name of the file which contains gene expression measurements. |

`sefile` |
character, the name of the file which contains the standard deviation of gene expression measurements. |

`subset` |
vector specifying the row number of genes which are clustered on. |

`gsnorm` |
logical specifying whether do global scaling normalisation or not. |

`mincls` |
integer, the minimum number of clusters. |

`maxcls` |
integer, the maximum number of clusters. |

`conds` |
integer, the number of conditions. |

`reps` |
vector, specifying which condition each column of the input data matrix belongs to. |

`verbose` |
logical value. If 'TRUE' messages about the progress of the function is printed. |

`eps` |
numeric, optimisation parameter. |

`del0` |
numeric, optimisation parameter. |

The input data is specified either by e and se, or by efile and sefile.

The result is a list with components

cluster: vector, containing the membership of clusters for each gene; centers: matrix, the center of each cluster; centersigs: matrix, the center variance of each cluster; likelipergene: matrix, the likelihood of belonging to each cluster for each gene; optK: numeric, the optimal number of clusters. optF: numeric, the maximised value of target function.

Xuejun Liu

Liu,X. and Rattray,M. (2009) Including probe-level measurement error in robust mixture clustering of replicated microarray gene expression, Statistical Application in Genetics and Molecular Biology, 9(1), Article 42.

Liu,X., Lin,K.K., Andersen,B., and Rattray,M. (2007) Propagating probe-level uncertainty in model-based gene expression clustering, BMC Bioinformatics, 8:98.

Liu,X., Milo,M., Lawrence,N.D. and Rattray,M. (2005) A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips, Bioinformatics, 21(18):3637-3644.

Related method `mmgmos`

and `pumaclust`

1 2 3 4 5 6 7 | ```
data(Clustii.exampleE)
data(Clustii.exampleStd)
r<-vector(mode="integer",0)
for (i in c(1:20))
for (j in c(1:4))
r<-c(r,i)
cl<-pumaClustii(Clustii.exampleE,Clustii.exampleStd,mincls=6,maxcls=6,conds=20,reps=r,eps=1e-3)
``` |

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