pumaClustii: Propagate probe-level uncertainty in robust t mixture...
In puma: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0)

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

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pumaClustii(e=NULL, se=NULL, efile=NULL, sefile=NULL, 
      subset=NULL, gsnorm=FALSE, mincls, maxcls, conds, reps, verbose=FALSE,
      eps=1.0e-5, del0=0.01)

`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

  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)