plotFeatureBoxplot {clusterExperiment} | R Documentation |
Plot a boxplot of the (transformed) values for a particular gene, separated by cluster
## S4 method for signature 'ClusterExperiment,character,ANY' plotFeatureBoxplot(object, whichCluster, feature, ...) ## S4 method for signature 'ClusterExperiment,missing,ANY' plotFeatureBoxplot(object, whichCluster, feature, ...) ## S4 method for signature 'ClusterExperiment,numeric,character' plotFeatureBoxplot(object, whichCluster, feature, ...) ## S4 method for signature 'ClusterExperiment,numeric,numeric' plotFeatureBoxplot(object, whichCluster, feature, plotUnassigned = FALSE, unassignedColor = NULL, missingColor = NULL, main = NULL, assay = NULL, ...)
object |
a ClusterExperiment object |
whichCluster |
which clusters to show on the plot |
feature |
identification of feature to plot, either row name or index |
... |
arguments passed to |
plotUnassigned |
whether to plot the unassigned samples as a cluster (either -1 or -2) |
unassignedColor |
If not NULL, should be character value giving the
color for unassigned (-2) samples (overrides |
missingColor |
If not NULL, should be character value giving the color
for missing (-2) samples (overrides |
main |
title of plot. If NULL, given default title. |
assay |
Identifies which assay in the |
A plot is created. The output of boxplot is returned
#clustering using pam: try using different dimensions of pca and different k data(simData) cl <- clusterMany(simData, nReducedDims=c(5, 10, 50), reducedDim="PCA", clusterFunction="pam", ks=2:4, findBestK=c(TRUE,FALSE), removeSil=c(TRUE,FALSE)) clusterLegend(cl)[[1]][,"name"]<-letters[1:nClusters(cl,ignoreUnassigned = FALSE)[1]] plotFeatureBoxplot(cl,feature=1)