ClusterMatch-class: ClusterMatch: A class representing matching of...
In flowMatch: Matching and meta-clustering in flow cytometry

Description Creating Object Slots Accessors Methods Author(s) See Also Examples

An object of class "ClusterMatch" represents matching of cluster/meta-clusters across a pair of FC samples/templates. A cluster (meta-cluster) from a sample (template) can match to zero, one or more than one cluster (meta-clusters) in another sample (template).

An object of class "ClusterMatch" is usually created by calling the function match.clusters:

match.clusters(object1, object2, dist.type='Mahalanobis', unmatch.penalty=999999).

Here, object1 and object2 are two objects of class ClusteredSample or Template between which the clusters or meta-clusters are matched. See the example section and also the match.clusters function for more details.

Unless you know exactly what you are doing, creating an object of class "ClusterMtach" using new or using the constructor is discouraged.

Let S1 and S2 be two FC samples or templates with k1 and k2 clusters or meta-clusters respectively. Then the matching of clusters (meta-clusters) across S1 and S2 is represented by an object of class "ClusterMatch" that contains the following slots:

match12:: A list of length k1 where match12[[i]] stores the indices of clusters (meta-clusters) from S2 matched to the i-th clustrer (meta-cluster) of S1. match12[[i]]=NULL if the i-th cluster (meta-cluster) of S1 remains unmatched, otherwise, it stores a vector of matched clusters (meta-clusters) from S2.
match21:: A list of length k2 where match21[[i]] stores the indices of clusters (meta-clusters) from S1 matched to the i-th clustrer (meta-cluster) of S2. match21[[i]]=NULL if the i-th cluster (meta-cluster) of S2 remains unmatched, otherwise, it stores a vector of matched clusters (meta-clusters) from S1.
matching.cost:: The cost of matching clusters (meta-clusters) across the samples. It is equal to the summation of dissimilarities of the matched clusters (meta-clusters) and penalty for the unmatched clusters (meta-clusters).
unmatch.penalty:: A numeric value denoting the penalty for leaving a cluster (meta-cluster) unmatched. If we set it to a a very large value then no cluster (meta-cluster) remains unmatched giving an edge cover solution.

All the slot accessor functions take an object of class ClusterMatch. I show usage of the first accessor function. Other functions can be called similarly.

get.match12:

Returns the matching from cluster in sample 1 to clusters in sample 2. See the slot description for details. Usage: get.match12(object)

here object is a ClusterMatch object.

get.match21:

Returns the matching from cluster in sample 2 to clusters in sample 1. See the slot description for details.

get.matching.cost:

Returns the total cost of matching clusters (meta-clusters) across the pair samples/templates.

get.unmatch.penalty:

Returns the penalty for leaving a cluster (meta-cluster) unmatched.

show

Display details about the ClusterMatch object.

summary

Return descriptive summary of the matching of clusters (meta-clusters) across a pair of samples (templates). Shows both list and matrix format.

Usage: summary(ClusterMatch)

Ariful Azad

match.clusters, ClusteredSample, Template

## ------------------------------------------------
## load data and retrieve two samples
## ------------------------------------------------

library(healthyFlowData)
data(hd)
sample1 = exprs(hd.flowSet[[1]])
sample2 = exprs(hd.flowSet[[2]])

## ------------------------------------------------
## cluster sample using kmeans algorithm
## ------------------------------------------------

clust1 = kmeans(sample1, centers=4, nstart=20)
clust2 = kmeans(sample2, centers=4, nstart=20)
cluster.labels1 = clust1$cluster
cluster.labels2 = clust2$cluster

## ------------------------------------------------
## Create ClusteredSample object  
## and compute mahalanobis distance between two clsuters
## ------------------------------------------------

clustSample1 = ClusteredSample(labels=cluster.labels1, sample=sample1)
clustSample2 = ClusteredSample(labels=cluster.labels2, sample=sample2)
## compute the dissimilarity matrix
DM = dist.matrix(clustSample1, clustSample2, dist.type='Mahalanobis')

## ------------------------------------------------
## Computing matching of clusteres  
## An object of class "ClusterMatch" is returned 
## ------------------------------------------------

mec = match.clusters(clustSample1, clustSample2, dist.type="Mahalanobis", unmatch.penalty=99999)
## show the matching 
summary(mec)


## **********************************************************************
## ************** Now matching meta-clusters across templates ***********
## **********************************************************************

## ------------------------------------------------
## Retrieve each sample, clsuter it and store the
## clustered samples in a list
## ------------------------------------------------

cat('Clustering samples: ')
clustSamples = list()
for(i in 1:10) # read 10 samples and cluster them
{
  cat(i, ' ')
  sample1 = exprs(hd.flowSet[[i]])
  clust1 = kmeans(sample1, centers=4, nstart=20)
  cluster.labels1 = clust1$cluster
  clustSample1 = ClusteredSample(labels=cluster.labels1, sample=sample1)
  clustSamples = c(clustSamples, clustSample1)
}

## ------------------------------------------------
## Create two templates each from five samples
## ------------------------------------------------

template1 = create.template(clustSamples[1:5])
template2 = create.template(clustSamples[6:10])

## ------------------------------------------------
## Match meta-clusters across templates
## ------------------------------------------------

mec = match.clusters(template1, template2, dist.type="Mahalanobis", unmatch.penalty=99999)
summary(mec)

## ------------------------------------------------
## Another example of matching meta-clusters & clusters
## across a template and a sample
## ------------------------------------------------

mec = match.clusters(template1, clustSample1, dist.type="Mahalanobis", unmatch.penalty=99999)
summary(mec)