pumaClustii {puma} | R Documentation

## Propagate probe-level uncertainty in robust t mixture clustering on replicated gene expression data

### Description

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.

### Usage

    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)

### Arguments

`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.

### Details

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

### Value

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.

### Author(s)

Xuejun Liu

### References

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.

### See Also

Related method `mmgmos` and `pumaclust`

### Examples

      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)

[Package puma version 3.26.0 Index]