plotDendro {celda}R Documentation

Plots dendrogram of 'findMarkers' output

Description

Generates a dendrogram of the rules and performance (optional) of the decision tree generates by 'findMarkers'.

Usage

plotDendro(
  decisionTree,
  classLabel = NULL,
  addSensPrec = FALSE,
  maxFeaturePrint = 4,
  leafSize = 24,
  boxSize = 7,
  boxColor = "black"
)

Arguments

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'.

Value

A ggplot2 object

Examples

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)

[Package celda version 1.2.4 Index]