plotMarkerDendro: Plots dendrogram of _findMarkersTree_ output

In celda: CEllular Latent Dirichlet Allocation

Description

Generates a dendrogram of the rules and performance (optional) of the decision tree generated by findMarkersTree().

Usage

plotMarkerDendro(
  tree,
  classLabel = NULL,
  addSensPrec = FALSE,
  maxFeaturePrint = 4,
  leafSize = 10,
  boxSize = 2,
  boxColor = "black"
)

Arguments

tree	List object. The output of findMarkersTree()
classLabel	A character value. The name of a specific label to draw the path and rules. If NULL (default), the tree for all clusters is shown.
addSensPrec	Logical. Print training sensitivities and precisions for each cluster below leaf label? Default is FALSE.
maxFeaturePrint	Numeric value. Maximum number of markers to print at a given split. Default is 4.
leafSize	Numeric value. Size of text below each leaf. Default is 24.
boxSize	Numeric value. Size of rule labels. Default is 7.
boxColor	Character value. Color of rule labels. Default is black.

Value

A ggplot2 object

Examples

## Not run: 
# Generate simulated single-cell dataset using celda
sim_counts <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)

# Celda clustering into 5 clusters & 10 modules
cm <- celda_CG(sim_counts$counts, K = 5, L = 10, verbose = FALSE)

# Get features matrix and cluster assignments
factorized <- factorizeMatrix(sim_counts$counts, cm)
features <- factorized$proportions$cell
class <- celdaClusters(cm)

# Generate Decision Tree
DecTree <- findMarkersTree(features, class, threshold = 1)

# Plot dendrogram
plotMarkerDendro(DecTree)

## End(Not run)

celda documentation built on Nov. 8, 2020, 8:24 p.m.