plotMarkerDendro {celda} | R Documentation

## Plots dendrogram of findMarkersTree output

### 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)

[Package celda version 1.6.1 Index]