dist.template {flowMatch} | R Documentation |
Compute the dissimilarity between a pair of FC templates by using Mixed Edge Cover (MEC) algorithm.
dist.template(template1, template2, dist.type='Mahalanobis', unmatch.penalty=999999)
template1 |
an object of class |
template2 |
an object of class |
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. |
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.
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.
Ariful Azad
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.
## ------------------------------------------------ ## 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)