makeClusters {clusterSeq} | R Documentation

## Creates clusters from a co-expression minimal linkage data.frame.

### Description

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, see the `makeClustersFF` function.

### Usage

    makeClusters(aM, cD, threshold = 0.5)

### Arguments

`aM` | A data frame constructed by `associatePosteriors` or `kCluster`, defining for each gene the nearest neighbour of higher row index and the dissimilarity with that neighbour.
`cD` | The data given as input to `associatePosteriors` or `kCluster` that produced 'aM'.
`threshold` | A threshold on the maximum dissimilarity at which two genes can cluster. Defaults to 0.5.

### Value

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.

### Author(s)

Thomas J Hardcastle

### See Also

`makeClustersFF` `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.
    normRT <- normRT[1:1000,]
    kClust <- kCluster(normRT)

    # make the clusters from these data.
    mkClust <- makeClusters(kClust, normRT, threshold = 1)

[Package clusterSeq version 1.14.0 Index]