pumaClust {puma} | R Documentation

## Propagate probe-level uncertainty in model-based clustering on gene expression data

### Description

This function clusters gene expression using a Gaussian mixture model including probe-level measurement error. 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

    pumaClust(e=NULL, se=NULL, efile=NULL, sefile=NULL,
          subset=NULL, gsnorm=FALSE, clusters,
          iter.max=100, nstart=10, eps=1.0e-6, del0=0.01)

### Arguments

`e` | either a valid `ExpressionSet` object, or a 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.
`clusters` | integer, the number of clusters.
`iter.max` | integer, the maximum number of iterations allowed in the parameter initialisation.
`nstart` | integer, the number of random sets chosen in the parameter initialisation.
`eps` | numeric, optimisation parameter.
`del0` | numeric, optimisation parameter.

### Details

The input data is specified either as an `ExpressionSet` object (in which case se, efile and sefile will be ignored), or 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; bic: numeric, the Bayesian Information Criterion score.

### Author(s)

Xuejun Liu, Magnus Rattray

### References

Liu,X., Lin,K.K., Andersen,B., and Rattray,M. (2006) 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 `pumaClustii`

### Examples

      data(Clust.exampleE)
      data(Clust.exampleStd)
      pumaClust.example<-pumaClust(Clust.exampleE,Clust.exampleStd,clusters=7)

[Package puma version 3.29.1 Index]