create.template {flowMatch} | R Documentation |
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.
create.template(clustSamples, dist.type = "Mahalanobis", unmatch.penalty=999999, template.id = NA_integer_)
clustSamples |
A list of |
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_ |
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.
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.
Ariful Azad
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.
## ------------------------------------------------ ## 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)