clusterByQuality: Cluster based on quality variables
In AneuFinder: Analysis of Copy Number Variation in Single-Cell-Sequencing Data

Description Usage Arguments Details Value Author(s) See Also Examples

This function uses the mclust package to cluster the input samples based on various quality measures.

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clusterByQuality(hmms, G = 1:9, itmax = c(100, 100),
  measures = c("spikiness", "entropy", "num.segments", "bhattacharyya",
  "complexity", "sos"), orderBy = "spikiness", reverseOrder = FALSE)

`hmms`	A list of `aneuHMM` objects or a character vector with files that contain such objects.
`G`	An integer vector specifying the number of clusters that are compared. See `Mclust` for details.
`itmax`	The maximum number of outer and inner iterations for the `Mclust` function. See `emControl` for details.
`measures`	The quality measures that are used for the clustering. Supported is any combination of `c('spikiness','entropy','num.segments','bhattacharyya','loglik','complexity','sos','avg.read.count','total.read.count','avg.binsize')`.
`orderBy`	The quality measure to order the clusters by. Default is `'spikiness'`.
`reverseOrder`	Logical indicating whether the ordering by `orderBy` is reversed.

Please see getQC for a brief description of the quality measures.

A list with the classification, parameters and the Mclust fit.

Aaron Taudt

getQC

## Get a list of HMMs
folder <- system.file("extdata", "primary-lung", "hmms", package="AneuFinderData")
files <- list.files(folder, full.names=TRUE)
cl <- clusterByQuality(files)
## Plot the clustering and print the parameters
plot(cl$Mclust, what='classification')
print(cl$parameters)
## Select files from the best 2 clusters for further processing
best.files <- unlist(cl$classification[1:2])