plotCluster: Plots data from clusterings.

In tjh48/clusterSeq: Clustering of high-throughput sequencing data by identifying co-expression patterns

Description

Given clusterings and expression data, plots representative expression data for each clustering.

Usage

plotCluster(cluster, cD, sampleSize = 1000)

Arguments

cluster	A list object defining the clusters, produced by makeClusters or makeClustersFF.
cD	The data object used to produce the clusters.
sampleSize	The maximum number of genes that will be ploted.

Details

Expression data are normalised and rescaled before plotting.

Value

Plotting function.

Author(s)

Thomas J Hardcastle

See Also

makeClusters makeClustersFF

Examples

# 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, threshold = 0.5)

# plot first six clusters
par(mfrow = c(2,3))
plotCluster(sX[1:6], cD.ratThymus)

tjh48/clusterSeq documentation built on May 31, 2019, 3:40 p.m.