| makeClustersFF {clusterSeq} | R Documentation |
This function uses the complete pairwise dissimilarity scores to construct a hierarchical clustering of the genes.
makeClustersFF(file, method = "complete", cut.height = 5)
file |
Filename containing the dissimilarity data. |
method |
Method to use in |
cut.height |
Cut height to use in |
An IntegerList object containing the clusters derived from a cut hierarchical clustering.
Thomas J Hardcastle
makeClusters
kCluster
associatePosteriors
#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. For speed, one thousand bootstraps are
# used, but higher values should be used in real analyses.
# Write full dissimilarity matrix to file "kclust.gz"
normRT <- normRT[1:1000,]
kClust <- kCluster(normRT, B = 1000, matrixFile = "kclust.gz")
# make the clusters from these data.
mkClustR <- makeClustersFF("kclust.gz")