| plotDendro {celda} | R Documentation |
Generates a dendrogram of the rules and performance (optional) of the decision tree generates by 'findMarkers'.
plotDendro( decisionTree, classLabel = NULL, addSensPrec = FALSE, maxFeaturePrint = 4, leafSize = 24, boxSize = 7, boxColor = "black" )
decisionTree |
List object. The output of 'celda::findMarkers'. |
classLabel |
A character value. The name of a label to which draw the path and rules. If NULL (default), the rules for every cluster is shown. |
addSensPrec |
Logical. Print training sensitivities and precisions for each cluster below leaf label? Default is FALSE. |
maxFeaturePrint |
A numeric value. Maximum number of feature IDs to print at a given node. Default is 4. |
leafSize |
A numeric value. Size of text below each leaf. Default is 24. |
boxSize |
A numeric value. Size of rule labels. Default is 7. |
boxColor |
A character value. Color of rule labels. Default is 'black'. |
A ggplot2 object
library(M3DExampleData)
counts <- M3DExampleData::Mmus_example_list$data
# Subset 500 genes for fast clustering
counts <- as.matrix(counts[1501:2000, ])
# Cluster genes ans samples each into 10 modules
cm <- celda_CG(counts = counts, L = 10, K = 5, verbose = FALSE)
# Get features matrix and cluster assignments
factorized <- factorizeMatrix(counts, cm)
features <- factorized$proportions$cell
class <- clusters(cm)$z
# Generate Decision Tree
decTree <- findMarkers(features,
class,
oneoffMetric = "modified F1",
threshold = 1,
consecutiveOneoff = FALSE)
# Plot dendrogram
plotDendro(decTree)