Plot validation results in an interactive graph

Source: R/plotValidate.R

There are three main information on the graph:

  • x-axis : Pearson correlation coefficient. Higher value means that test dataset and RAV is more tightly associated with.

  • y-axis : Silhouette width representing the quality of RAVs.

  • size : The number of studies in each RAV. (= cluster size)

  • color : Test dataset's PC number that validate each RAV. Because we used top 8 PCs of the test dataset, there are 8 categories.

plotValidate(
  val_all,
  minClusterSize = 2,
  swFilter = FALSE,
  minSilhouetteWidth = 0,
  interactive = FALSE,
  minClSize = NULL,
  maxClSize = NULL,
  colorPalette = "Dark2"
)

Arguments

val_all

Output from validate function.

minClusterSize

The minimum size of clusters to be included in the plotting. Default value is 2, so any single-element clusters are excluded.

swFilter

If swFilter=TRUE, only RAV above the cutoff, defined through minSilhouetteWidth argument will be plotted. Default is swFilter=FALSE

minSilhouetteWidth

A minimum average silhouette width to be plotted. Only effective under swFilter=TRUE condition. Default is 0.

interactive

If set to TRUE, the output will be interactive plot. Default is FALSE.

minClSize

The minimum number of PCs in the clusters you want.

maxClSize

The maximum number of PCs in the clusters you want.

colorPalette

Default is Dark2. For other color options, please check scale_color_brewer

Value

a ggplot object

Examples

data(miniRAVmodel)
library(bcellViper)
data(bcellViper)
val_all <- validate(dset, miniRAVmodel)
plotValidate(val_all)