plotMarkerDendro {celda} | R Documentation |
Generates a dendrogram of the rules and performance (optional) of the decision tree generated by findMarkersTree().
plotMarkerDendro( tree, classLabel = NULL, addSensPrec = FALSE, maxFeaturePrint = 4, leafSize = 10, boxSize = 2, boxColor = "black" )
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. |
A ggplot2 object
## 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)