This function uses minimal linkage data to perform rapid clustering by
singleton agglomeration (i.e., a gene will always cluster with its
nearest neighbours provided the distance to those neighbours does not
exceed some threshold). For alternative (but slower) clustering options,
makeClusters(aM, cD, threshold = 0.5)
A data frame constructed by
The data given as input to
A threshold on the maximum dissimilarity at which two genes can cluster. Defaults to 0.5.
An IntegerList object, each member of whom defines a cluster of co-expressed genes. The object is ordered decreasingly by the size of each cluster.
Thomas J Hardcastle
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#Load in the processed data of observed read counts at each gene for each sample. data(ratThymus, package = "clusterSeq") # Library scaling factors are acquired here using the getLibsizes # function from the baySeq package. libsizes <- getLibsizes(data = ratThymus) # Adjust the data to remove zeros and rescale by the library scaling # factors. Convert to log scale. ratThymus[ratThymus == 0] <- 1 normRT <- log2(t(t(ratThymus / libsizes)) * mean(libsizes)) # run kCluster on reduced set. normRT <- normRT[1:1000,] kClust <- kCluster(normRT) # make the clusters from these data. mkClust <- makeClusters(kClust, normRT, threshold = 1)
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