wallace {clusterSeq} | R Documentation

## Computes Wallace scores comparing two clustering methods.

### Description

Given two clusterings A \& B we can calculate the likelihood that two elements are in the same cluster in B given that they are in the same cluster in A, and vice versa.

### Usage

    wallace(v1, v2)

### Arguments

`v1` | SimpleIntegerList object (output from makeClusters or makeClustersFF).
`v2` | SimpleIntegerList object (output from makeClusters or makeClustersFF).

### Value

Vector of length 2 giving conditional likelihoods.

### Author(s)

Thomas J. Hardcastle

### Examples

    # using likelihood data from a Bayesian analysis of the data

    # load in analysed countData object
    data(cD.ratThymus, package = "clusterSeq")

    # estimate likelihoods of dissimilarity on reduced set
    aM <- associatePosteriors(cD.ratThymus[1:1000,])

    # make clusters from dissimilarity data
    sX <- makeClusters(aM, cD.ratThymus[1:1000,], threshold = 0.5)

    # or using k-means clustering on raw count data

    #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, replicates = cD.ratThymus@replicates)

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

    # compare clusterings
    wallace(sX, mkClust)

[Package clusterSeq version 1.4.0 Index]