Creating a template of a collection of FC samples

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Description

Create an object of class Template summarizes a group of samples belonging to same biological-class with a class-template. A template is represented by a collection of meta-clusters (MetaCluster) created from samples of same class. An object of class Template therefore stores a list of MetaCluster objects and other necessary parameters.

Usage

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  create.template(clustSamples, dist.type = "Mahalanobis", unmatch.penalty=999999, template.id = NA_integer_)

Arguments

clustSamples

A list of ClusteredSample objects from which the template is created. The working examples describe how this objects are created by clustering FC samples.

dist.type

character, indicating the method with which the dissimilarity between a pair of clusters is computed. Supported dissimilarity measures are: 'Mahalanobis', 'KL' and 'Euclidean'. If this argument is not passed then 'Mahalanobis' distance is used by default.

unmatch.penalty

A numeric value denoting the penalty for leaving a 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 remains unmatched.

template.id

integer, denoting the index of the template (relative to other template). Default is NA_integer_

Details

An object of class Template summarizes a group of samples belonging to same biological-class with a class-specific template. A template is represented by a collection of meta-clusters (MetaCluster) created from samples of same class. An object of class Template therefore stores a list of MetaCluster objects and other necessary parameters.

Value

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

Author(s)

Ariful Azad

References

Azad, Ariful and Pyne, Saumyadipta and Pothen, Alex (2012), Matching phosphorylation response patterns of antigen-receptor-stimulated T cells via flow cytometry; BMC Bioinformatics, 13 (Suppl 2), S10.

See Also

Template, MetaCluster

Examples

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 ## ------------------------------------------------
## load data
## ------------------------------------------------

library(healthyFlowData)
data(hd)

## ------------------------------------------------
## Retrieve each sample, clsuter it and store the
## clustered samples in a list
## ------------------------------------------------
set.seed(1234) # for reproducable clustering 
cat('Clustering samples: ')
clustSamples = list()
for(i in 1:length(hd.flowSet))
{
  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 a template from the list of clustered samples and plot functions
## ------------------------------------------------

template = create.template(clustSamples)
summary(template)

## plot the tree denoting the hierarchy of the samples in a template
tree = template.tree(template)


## plot the template in terms of the meta-clusters
## option-1 (default): plot contours of each cluster of the meta-clusters
plot(template)

## option-2: plot contours of each cluster of the meta-clusters with defined color
plot(template, color.mc=c('blue','black','green3','red'))

## option-3: plot contours of the meta-clusters with defined color
plot(template, plot.mc=TRUE, color.mc=c('blue','black','green3','red'))


## option-4: plot contours of each cluster of the meta-clusters with different colors for different samples
plot(template, colorbysample=TRUE)