segmentCluster.batch: Batch wrapper for 'segmentClusters'.
In segmenTier: Similarity-Based Segmentation of Multidimensional Signals

Description Usage Arguments Details Value Examples

A high-level wrapper for multiple runs of segmentation by segmentClusters for multiple clusterings and/or multiple segmentation parameters. It additionally allows to tag adjacent segments to be potentially fused due to similarity of their clusters.

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segmentCluster.batch(cset, varySettings = setVarySettings(),
  fuse.threshold = 0.2, rm.nui = TRUE, type.name, short.name = TRUE,
  id, save.matrix = FALSE, verb = 1)

`cset`	a clustering set as returned by `clusterTimeseries`
`varySettings`	list of settings where each entry can be a vector; the function will construct a matrix of all possible combinations of parameter values in this list, call `segmentClusters` for each, and report a matrix of segments where the segment ‘type’ is constructed from the varied parameters; see option `short.name`. A varySettings list with all required (default) parameters can be obtained via function `setVarySettings`.
`fuse.threshold`	if adjacent segments are associated with clusters the centers of which have a Pearson correlation `>fuse.threshold` the field "fuse" will be set to 1 for the second segments (top-to-bottom as reported)
`rm.nui`	remove nuisance cluster segments from final results
`type.name`	vector of strings selecting the parameters which will be used as segment types. Note, that all parameters that are actually varied will be automatically added (if missing). The list can include parameters from time-series processing found in the "clustering" object `cset` as `cset$tids`.
`short.name`	default type name construction; if TRUE (default) parameters that are not varied will not be part of the segment type and ID. This argument has no effect if argument `type.name` is set.
`id`	if set, the default segment IDs, constructed from numbered segment types, are replaced by this
`save.matrix`	store the total score matrix `S(i,c)` and the backtracing matrix `K(i,c)`; useful in testing stage or for debugging or illustration of the algorithm; TODO: save.matrix is currently not implemented, since batch function returns a matrix only
`verb`	level of verbosity, 0: no output, 1: progress messages

This is a high-level wrapper for segmentClusters which allows segmentation over multiple clusterings as provided by the function clusterTimeseries and over multiple segmentation parameters. Each parameter in the list varySettings can be a vector and ALL combinations of the passed parameter values will be used for one run of segmentClusters. The resulting segment table, list item "segments" of the returned object, is a data.frame with additional columns "ID" and "type", automatically generated strings indicating the used parameters (each "type" reflects one parameter set), and "colors", indicating the automatically generated color of the assigned cluster label.

Returns an object of class "segments", just as its base function segmentClusters, but the main segment table, list item "segments", is a data.frame with additional columns "ID" and "type", automatically generated strings indicating the used parameters (each "type" reflects one parameter set), and "colors", indicating the automatically generated color of the assigned cluster label.

# load example data, an RNA-seq time-series data from a short genomic
# region of budding yeast
data(primseg436)

# 1) Fourier-transform time series:
tset <- processTimeseries(ts=tsd, na2zero=TRUE, use.fft=TRUE,
                          dft.range=1:7, dc.trafo="ash", use.snr=TRUE)

# 2) cluster time-series several times into K=12 clusters:
cset <- clusterTimeseries(tset, K=c(12,12,12))

# 3) choose parameter ranges, here only E is varied 
vary <- setVarySettings(M=100, E=c(1,3), nui=3, S="icor", Mn=20)

# 4) ... segment ALL using the batch function:
## Not run:  ## NOTE: takes too long for CRAN example timing restrictions
segments <- segmentCluster.batch(cset=cset, varySettings=vary)

# 5) inspect results:
print(segments)
plotSegmentation(tset, cset, segments)

# 6) and get segment border table. Note that the table has
#    additional columns "ID" and "type", indicating the used parameters,
#    and "color" providing the color of the cluster the segment was
#    assigned to. This allows to track segments in the inspection plots.
sgtable <- segments$segments

## End(Not run)