wallace {clusterSeq} | R Documentation |
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.
wallace(v1, v2)
v1 |
SimpleIntegerList object (output from makeClusters or makeClustersFF). |
v2 |
SimpleIntegerList object (output from makeClusters or makeClustersFF). |
Vector of length 2 giving conditional likelihoods.
Thomas J. Hardcastle
# 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)