dist.matrix: Dissimilarity matrix between each pair of clusters/meta-clusters across a pair of samples/templates

Description

Calculate a matrix storing the dissimilarities between each pair of clusters (meta-clusters) across a pair of samples (templates) S1 and S2. (i,j)th entry of the matrix stores dissimilarity between i-th cluster (meta-cluster) from S1 and the j-th cluster (meta-cluster) from S2.

Usage

dist.matrix(object1,object2, dist.type = 'Mahalanobis')

Arguments

object1

an object of class ClusteredSample or Template.

object2

an object of class ClusteredSample or Template.

dist.type

character, indicating the method with which the dissimilarity between a pair of clusters (meta-clusters) is computed. Supported dissimilarity measures are: 'Mahalanobis', 'KL' and 'Euclidean', with the default is set to 'Mahalanobis' distance.

Value

dist.matrix function returns a (k1 x k2) matrix where k1 and k2 are the number of clusters (meta-clusters) in the first and the second samples (templates) respectively. (i,j)th entry of the matrix contains the dissimilarity between the i-th cluster (meta-cluster) from sample1 (template1) and the j-th cluster (meta-cluster) from sample2 (template2).

Details

Consider two FC samples/templates S1 and S2 with k1 and k2 clusters/meta-clusters. The dissimilarity between each pair of cluster (meta-clusters) across S1 and S2 is computed and stored in a (k1 x k2) matrix. The dissimilarity between i-th cluster (meta-cluster) from S1 and j-th cluster (meta-cluster) from S2 is computed using function dist.cluster.

Examples

Run this code

## ------------------------------------------------
## 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 the Mahalanobis distance between 
## each pair of clsuters and save it in a matrix
## ------------------------------------------------

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')
print(DM)