plot one single tree with the optimal clustering result

after an optimal cluster has been identified, users may use this function to plot the resulting dendrogram with the branch colors represent clutering results

plot_optimal_CORE(
  original_tree,
  optimal_cluster = NULL,
  shift = -100,
  values = NULL
)

Arguments

original_tree	a dendrogram object
optimal_cluster	a vector of cluster IDs for cells in the dendrogram
shift	a numer specifying the gap between the dendrogram and the colored
values	a vector containing color values of the branches and the colored bar underneath the tree bar underneath the dendrogram. This parameter allows better selection of colors for the display.

Value

a plot with colored braches and bars for the optimal clustering result

Author

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
CORE_cluster <- CORE_clustering(mixedpop2, remove_outlier = c(0))
#> Performing 1 round of filtering
#> Identifying top variable genes
#> Calculating distance matrix
#> Performing hierarchical clustering
#> Finding clustering information
#> No more outliers detected in filtering round 1
#> Identifying top variable genes
#> Calculating distance matrix
#> Performing hierarchical clustering
#> Finding clustering information
#> Done clustering, moving to stability calculation...
#> Done calculating stability...
#> Start finding optimal clustering...
#> Done finding optimal clustering...
key_height <- CORE_cluster$optimalClust$KeyStats$Height
optimal_res <- CORE_cluster$optimalClust$OptimalRes
optimal_index = which(key_height == optimal_res)
plot_optimal_CORE(original_tree= CORE_cluster$tree, 
    optimal_cluster = unlist(CORE_cluster$Cluster[optimal_index]),
    shift = -2000)
#> Ordering and assigning labels...
#> 2
#> 162335NA
#> 3
#> 162335423
#> Plotting the colored dendrogram now....
#> Plotting the bar underneath now....