pumaClustii {puma} | R Documentation |
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.
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)