plotClustersWorkflow,ClusterExperiment-method {clusterExperiment} | R Documentation |
A realization of plotClusters
call specific to
separating out the results of clusterMany
and other clustering
results.
## S4 method for signature 'ClusterExperiment' plotClustersWorkflow(object, whichClusters = c("mergeClusters", "makeConsensus"), whichClusterMany = NULL, nBlankLines = ceiling(nClusterings(object) * 0.05), existingColors = c("ignore", "all", "highlightOnly"), nSizeResult = ceiling(nClusterings(object) * 0.02), clusterLabels = TRUE, clusterManyLabels = TRUE, sortBy = c("highlighted", "clusterMany"), highlightOnTop = TRUE, ...)
object |
A |
whichClusters |
which clusterings to "highlight", i.e draw separately
from the bulk of the plot, see argument |
whichClusterMany |
indicate which clusterings to plot in the bulk of the
plot, see argument |
nBlankLines |
the number of blank (i.e. white) rows to add between the clusterMany clusterings and the highlighted clusterings. |
existingColors |
one of "ignore","all","highlightOnly". Whether the plot
should use the stored colors in the |
nSizeResult |
the number of rows each highlighted clustering should take up. Increasing the number increases the thickness of the rectangles representing the highlighted clusterings. |
clusterLabels |
either logical, indicating whether to plot the labels
for the clusterings identified to be highlighted in the
|
clusterManyLabels |
either logical, indicating whether to plot the
labels for the clusterings from clusterMany identified in the
|
sortBy |
how to align the clusters. If "highlighted" then the
highlighted clusters indicated in the argument |
highlightOnTop |
logical. Whether the highlighted clusters should be plotted on the top of clusterMany results or underneath. |
... |
arguments passed to the matrix version of
|
This plot is solely intended to make it easier to use the
plotClusters
visualization when there are a large number of
clusterings from a call to clusterMany
. This plot separates
out the clusterMany
results from a designated clustering of
interest, as indicated by the whichClusters
argument
(by default clusterings from a call to makeConsensus
or
mergeClusters
). In addition the highlighted clusters are
made bigger so that they can be easily seen.
A plot is produced, nothing is returned.
#clustering using pam: try using different dimensions of pca and different k data(simData) cl <- clusterMany(simData, nReducedDims=c(5, 10, 50), reduceMethod="PCA", clusterFunction="pam", ks=2:4, findBestK=c(TRUE,FALSE), removeSil=c(TRUE,FALSE)) cl <- makeConsensus(cl, proportion=0.7) plotClustersWorkflow(cl)