plot_optimal_CORE: plot one single tree with the optimal clustering result

In IMB-Computational-Genomics-Lab/scGPS: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

Description

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

Usage

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(s)

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$dat5_clusters)
CORE_cluster <- CORE_clustering(mixedpop2, remove_outlier = c(0))
key_height <- CORE_cluster$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)

IMB-Computational-Genomics-Lab/scGPS documentation built on Dec. 6, 2020, 3:20 p.m.