# dist.matrix: Dissimilarity matrix between each pair of... In flowMatch: Matching and meta-clustering in flow cytometry

## 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

 `1` ```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.

## 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`.

## 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).

## Author(s)

`dist.cluster`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ```## ------------------------------------------------ ## 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) ```