dist.template: Dissimilarity between a pair of FC templates

In flowMatch: Matching and meta-clustering in flow cytometry

Description

Compute the dissimilarity between a pair of FC templates by using Mixed Edge Cover (MEC) algorithm.

Usage

dist.template(template1, template2, dist.type='Mahalanobis', unmatch.penalty=999999)

Arguments

template1	an object of class Template containing cell populations from template 1.
template2	an object of class Template containing cell populations from template 2.
dist.type	character, indicating the method with which the dissimilarity between a pair of meta-clusters is computed. Supported dissimilarity measures are: 'Mahalanobis', 'KL' and 'Euclidean'.
unmatch.penalty	A numeric value denoting the penalty for leaving a 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 meta-cluster remains unmatched.

Details

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

Value

dist.template returns a numeric value representing dissimilarity between a pair of templates. This value is equal to the summation of dissimilarities of the matched meta-clusters and penalty for the unmatched meta-clusters.

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

Template, match.clusters

Examples

## ------------------------------------------------
## load data and retrieve two templates
## ------------------------------------------------

library(healthyFlowData)
data(hd)

## ------------------------------------------------
## 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])

D = dist.template(template1, template2, dist.type='Mahalanobis', unmatch.penalty=999999)

flowMatch documentation built on Nov. 8, 2020, 8:02 p.m.