match.clusters {flowMatch} | R Documentation

## Matching of clusters/meta-clusters across FC samples/templates

### Description

This function computes a 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 clusters (meta-clusters) in another sample (template).

### Usage

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

    match.clusters.dist(d.matrix,unmatch.penalty=999999)

### 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.
`d.matrix` | a matrix used only in the second defination (`match.clusters.dist`) of the function. `d.matrix` stores the dissimilarities between each pair of clusters (meta-clusters) across a pair of samples (templates) `S1` and `S2`. `(i,j)` entry of the matrix stores dissimilarity between `i-th` cluster (meta-cluster) from `S1` and the `j-th` cluster (meta-cluster) from `S2`. `d.matrix` can be computed using function `dist.matrix`
`unmatch.penalty` | numeric value denoting the penalty for leaving a cluster (meta-cluster) unmatched. This parameter should be already known or be estimated empirically estimated from data (see the reference for a discussion). Default is set to a very high value so that no cluster (meta-cluster) remains unmatched.

### Details

We used a robust version of matching called Mixed Edge Cover (MEC) to match clusters (meta-clusters) across a pair of samples (templates). MEC allows a cluster (meta-cluster) to be matched with zero, one or more than one clusters (meta-clusters) across a pair of samples (template). The cost of an MEC solution is equal to the summation of dissimilarities of the matched clusters (meta-clusters) and penalty for the unmatched clusters (meta-clusters). The MEC algorithm finds an optimal solution by minimizing the cost of MEC.

### Value

`match.clusters` returns an object of class `ClusterMatch` representing matching of clusters (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).

### Author(s)

Ariful Azad

### References

Azad, Ariful and Langguth, Johannes and Fang, Youhan and Qi, Alan and Pothen, Alex (2010), Identifying rare cell populations in comparative flow cytometry; Algorithms in Bioinformatics, Springer, 162-175.

### See Also

`dist.matrix`, `ClusteredSample`, `Template`

### Examples

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

    library(healthyFlowData)
    data(hd)

    ## **********************************************************************
    ## ************** first matching clusters across samples ****************
    ## **********************************************************************

    ## ------------------------------------------------
    ## retrieve and cluster two samples using kmeans algorithm
    ## ------------------------------------------------
    sample1 = exprs(hd.flowSet[[1]])
    sample2 = exprs(hd.flowSet[[2]])

    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
    ## ------------------------------------------------

    ## directly from the ClusteredSample objects: approach 1
    mec = match.clusters(clustSample1, clustSample2, dist.type="Mahalanobis", unmatch.penalty=99999)
    ## from the dissimilarity matrix: approach 2
    mec = match.clusters.dist(DM, 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)

[Package flowMatch version 1.16.0 Index]