| dist.matrix {flowMatch} | R Documentation |
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.
dist.matrix(object1,object2, dist.type = 'Mahalanobis')
object1 |
an object of class |
object2 |
an object of class |
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. |
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.
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).
Ariful Azad
## ------------------------------------------------ ## 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)