citrus.plotHierarchicalClusterFeatureGroups | R Documentation |
Plot clustering heirarchy with a subset of clusters (clusters of interest) highlighted by color and/or encircled.
citrus.plotHierarchicalClusterFeatureGroups(outputFile, featureClusterMatrix,
graph, layout, theme = "black", plotSize = 15, plotClusterIDs = T,
featureClusterColors = NULL, encircle = T)
outputFile |
Full path to output file (should end in '.pdf'). |
featureClusterMatrix |
Matrix of clusters to encircle. See details. |
graph |
Graph object to be plotted |
layout |
Layout for graph |
theme |
General color theme for plot. Options are |
plotSize |
Size of square pdf (inches). |
plotClusterIDs |
Plot cluster IDs on vertices? |
featureClusterColors |
Named vector of colors for each vertex. See details. |
encircle |
Should related highlighted clusters be encircled? |
The featureClusterMatrix
argument should be a two column matrix. The first column should be names 'cluster' and rows should contain
a cluster id to be highlighted. The second column should be named 'feature' and should contain a string or property describing
the general property of interest for this cluster. Entries having the same 'feature' value are plotted in the graph. One plot
is created for each unique value of the 'feature' column.
The featureClusterColors
argument can be used to supply custom colors for graph vertices. The vector should be a named
vector with each entry being a color value and the name of the entry should be a vertex id (cluster id) that will be colored.
Robert Bruggner
# Where the data lives
dataDirectory = file.path(system.file(package = "citrus"),"extdata","example1")
# Create list of files to be analyzed
fileList = data.frame("unstim"=list.files(dataDirectory,pattern=".fcs"))
# Read the data
citrus.combinedFCSSet = citrus.readFCSSet(dataDirectory,fileList)
# List of columns to be used for clustering
clusteringColumns = c("Red","Blue")
# Cluster data
citrus.clustering = citrus.cluster(citrus.combinedFCSSet,clusteringColumns)
# Large enough clusters
largeEnoughClusters = citrus.selectClusters(citrus.clustering)
# Create graph for plotting
hierarchyGraph = citrus.createHierarchyGraph(citrus.clustering,selectedClusters=largeEnoughClusters)
# Features to highlight
featureClusterMatrix = data.frame(cluster=c(19992,19978,19981,19987,19983,19973),feature=rep(c("Property 1","Property 2"),each=3))
# Plot features in clustering hierarchy
# citrus.plotHierarchicalClusterFeatureGroups(outputFile="/path/to/outputFile.pdf",featureClusterMatrix,graph=hierarchyGraph$graph,layout=hierarchyGraph$layout,plotSize=hierarchyGraph$plotSize)
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