makeClustersFF {clusterSeq} | R Documentation

## Creates clusters from a file containing a full dissimilarity matrix.

### 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")

[Package clusterSeq version 1.4.0 Index]