pumaClust {puma} | R Documentation |
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.
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)
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. |
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.
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.
Xuejun Liu, Magnus Rattray
Liu,X., Lin,K.K., Andersen,B., and Rattray,M. (2006) Propagating probe-level uncertainty in model-based gene expression clustering, technical report available upon request.
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
data(Clust.exampleE) data(Clust.exampleStd) pumaClust.example<-pumaClust(Clust.exampleE,Clust.exampleStd,clusters=7)