makeClustersFF: Creates clusters from a file containing a full dissimilarity...

In clusterSeq: Clustering of high-throughput sequencing data by identifying co-expression patterns

Description

This function uses the complete pairwise dissimilarity scores to construct a hierarchical clustering of the genes.

Usage

makeClustersFF(file, method = "complete", cut.height = 5)

Arguments

file	Filename containing the dissimilarity data.
method	Method to use in hclust.
cut.height	Cut height to use in hclust.

Value

An IntegerList object containing the clusters derived from a cut hierarchical clustering.

Author(s)

Thomas J Hardcastle

See Also

makeClusters kCluster associatePosteriors

Examples

#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")

clusterSeq documentation built on Nov. 8, 2020, 8:18 p.m.