Description Usage Arguments Details Value Author(s) References See Also Examples
Align multiple clusterings of the same set of samples and provide a colorcoded plot of their shared cluster assignments
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  ## S4 method for signature 'ClusterExperiment,character'
plotClusters(object,
whichClusters = c("workflow", "all"), ...)
## S4 method for signature 'ClusterExperiment,numeric'
plotClusters(object, whichClusters,
existingColors = c("ignore", "all", "firstOnly"), resetNames = FALSE,
resetColors = FALSE, resetOrderSamples = FALSE, sampleData = NULL,
clusterLabels = NULL, ...)
## S4 method for signature 'ClusterExperiment,missing'
plotClusters(object, whichClusters, ...)
## S4 method for signature 'matrix,missing'
plotClusters(object, whichClusters,
orderSamples = NULL, sampleData = NULL, reuseColors = FALSE,
matchToTop = FALSE, plot = TRUE, unassignedColor = "white",
missingColor = "grey", minRequireColor = 0.3, startNewColors = FALSE,
colPalette = massivePalette, input = c("clusters", "colors"),
clusterLabels = colnames(object), add = FALSE, xCoord = NULL,
ylim = NULL, tick = FALSE, ylab = "", xlab = "", axisLine = 0,
box = FALSE, ...)

object 
A matrix of with each column corresponding to a clustering
and each row a sample or a 
whichClusters 
If numeric, a predefined order for the clusterings in
the plot. If x is a 
... 
for 
existingColors 
how to make use of the exiting colors in the

resetNames 
logical. Whether to reset the names of the clusters in

resetColors 
logical. Whether to reset the colors in

resetOrderSamples 
logical. Whether to replace the existing

sampleData 
If 
clusterLabels 
names to go with the columns (clusterings) in matrix

orderSamples 
A predefined order in which the samples will be plotted.
Otherwise the order will be found internally by aligning the clusters
(assuming 
reuseColors 
Logical. Whether each row should consist of the same set of colors. By default (FALSE) each cluster that the algorithm doesn't identify to the previous rows clusters gets a new color. 
matchToTop 
Logical as to whether all clusters should be aligned to the first row. By default (FALSE) each cluster is aligned to the ordered clusters of the row above it. 
plot 
Logical as to whether a plot should be produced. 
unassignedColor 
If “1” in 
missingColor 
If “2” in clusters, will be given this color (meant for samples that were missing from the clustering, mainly when comparing clusterings run on different sets of samples) 
minRequireColor 
In aligning colors between rows of clusters, require this percent overlap. 
startNewColors 
logical, indicating whether in aligning colors between
rows of clusters, should the colors restart at beginning of colPalette as
long as colors are not in immediately proceeding row (the colors at the end
of 
colPalette 
a vector of colors used for the different clusters. Must be
as long as the maximum number of clusters found in any single
clustering/column given in 
input 
indicate whether the input matrix is matrix of integer assigned
clusters, or contains the colors. If 
add 
whether to add to existing plot. 
xCoord 
values on xaxis at which to plot the rows (samples). 
ylim 
vector of limits of yaxis. 
tick 
logical, whether to draw ticks on xaxis for each sample. 
ylab 
character string for the label of yaxis. 
xlab 
character string for the label of xaxis. 
axisLine 
the number of lines in the axis labels on yaxis should be
(passed to 
box 
logical, whether to draw box around the plot. 
All arguments of the matrix version can be passed to the
ClusterExperiment
version. As noted above, however, some arguments
have different interpretations.
If whichClusters = "workflow"
, then the workflow clusterings
will be plotted in the following order: final, mergeClusters, combineMany,
clusterMany.
If clusters
is a ClusterExperiment
Object, then
plotClusters
returns an updated ClusterExperiment
object,
where the clusterLegend
and/or orderSamples
slots have been
updated (depending on the arguments).
If clusters
is a matrix, plotClusters
returns
(invisibly) the orders and other things that go into making the matrix.
Specifically, a list with the following elements.
orderSamples
a vector of length equal to
nrows(clusters)
giving the order of the samples (rows) to
use to get the original clusters matrix
into the order made by plotClusters
.
colors
matrix of color assignments for each element of
original clusters matrix. Matrix is in the same order as original clusters
matrix. The matrix colors[orderSamples,]
is the matrix that can be
given back to plotClusters
to recreate the plot (see examples).
alignedClusterIds
a matrix of integer valued cluster
assignments that match the colors. This is useful if you want to have cluster
identification numbers that are better aligned than that given in the
original clusters. Again, the rows/samples are in same order as original
input matrix.
clusterLegend
list of length equal to the number of columns of
input matrix. The elements of the list are matrices, each with three columns
named "Original","Aligned", and "Color" giving, respectively, the
correspondence between the original cluster ids in clusters
, the
aligned cluster ids in aligned
, and the color.
origClusters
The original matrix of clusters given to
plotClusters
Elizabeth Purdom and Marla Johnson (based on the tracking plot in ConsensusClusterPlus by Matt Wilkerson and Peter Waltman).
Wilkerson, D. M, Hayes and Neil D (2010). "ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking." Bioinformatics, 26(12), pp. 15721573.
The ConsensusClusterPlus
package.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64  #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))
clusterLabels(cl)
#make names shorter for better plotting
x < clusterLabels(cl)
x < gsub("TRUE", "T", x)
x < gsub("FALSE", "F", x)
x < gsub("k=NA,", "", x)
x < gsub("Features", "", x)
clusterLabels(cl) < x
par(mar=c(2,10,1,1))
#this will make the choices of plotClusters
cl < plotClusters(cl, axisLine=1, resetOrderSamples=TRUE, resetColors=TRUE)
#see the new cluster colors
clusterLegend(cl)[1:2]
#We can also change the order of the clusterings. Notice how this
#dramatically changes the plot!
clOrder < c(3:6, 1:2, 7:ncol(clusterMatrix(cl)))
cl < plotClusters(cl, whichClusters=clOrder, resetColors=TRUE,
resetOrder=TRUE, axisLine=2)
#We can manually switch the red ("#E31A1C") and green ("#33A02C") in the
#first cluster:
#see what the default colors are and their names
showPalette(wh=1:5)
#change "#E31A1C" to "#33A02C"
newColorMat < clusterLegend(cl)[[clOrder[1]]]
newColorMat[2:3, "color"] < c("#33A02C", "#E31A1C")
clusterLegend(cl)[[clOrder[1]]]<newColorMat
#replot by setting 'input="colors"'
par(mfrow=c(1,2))
plotClusters(cl, whichClusters=clOrder, orderSamples=orderSamples(cl),
existingColors="all")
plotClusters(cl, whichClusters=clOrder, resetColors=TRUE, resetOrder=TRUE,
axisLine=2)
par(mfrow=c(1,1))
#set some of clusterings arbitrarily to "1", meaning not clustered (white),
#and "2" (another possible designation getting gray, usually for samples not
#included in original clustering)
clMatNew < apply(clusterMatrix(cl), 2, function(x) {
wh < sample(1:nSamples(cl), size=10)
x[wh]< 1
wh < sample(1:nSamples(cl), size=10)
x[wh]< 2
return(x)
})
#make a new object
cl2 < ClusterExperiment(assay(cl), clMatNew,
transformation=transformation(cl))
plotClusters(cl2)

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