clusterCons (version 1.2)

consmatrix-class: Class "consmatrix"

Description

Objects of class 'consmatrix' are created to hold the results of a consensus clustering experiment along with the necessary ancillary data to allow the subsequent downstream calculations such as cluster and membership robustness. In addition the object holds the original call made when running cluscomp.

Objects from the Class

Objects can be created by calls of the form new("consmatrix", ...), but are normally created internally by the cluscomp function to store consensus matrices and their associated meta-data.

Slots

cm:

Object of class "matrix" - the consensus matrix itself

rm:

Object of class "data.frame" - the cluster membership of the full (i.e. not consensus) clustering result when the current algorith is called with the same algorithm parameters as the consensus clustering run. This is needed to be able to work with merge matrices that need a clustering structure on which to operate to produce cluster and membership robustness values.

a:

Object of class "character" - the clustering algorithm name

k:

Object of class "numeric" - the cluster number (k) used

call:

Object of class "call" - the original parameters passed to cluscomp for provenance and reproducibility

References

Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.

See Also

See Also cluscomp

Examples

# NOT RUN {
showClass("consmatrix");

#you can access the slots in useful ways

#load a cmr
data(testcmr);

#get a consensus clustering matrix via the 'cm' slot
cm <- testcmr$e1_kmeans_k4@cm;

#this can be used as a distance matrix, e.g. for a heatmap
heatmap(cm);

#or as a new distance matrix
dm <- data.frame(cm) #first convert to a data.frame
#make sure names are the same for rows and columns
names(dm) <- row.names(dm);

#you need to explicitly tell cluscomp that you are passing a distance matrix
cmr2 <- cluscomp(dm,diss=TRUE,clmin=2,clmax=4,rep=2);

#for merge consensus clustering you take advantage of the reference matrix (rm) slot
#cluster robustness for agnes with cluster number (k) = 3
clrob(testcmr$merge_k3,testcmr$e1_kmeans_k3@rm);
#membership robustness for cluster 1
memrob(testcmr$merge_k3,testcmr$e1_kmeans_k3@rm)$cluster1;
# }