Graph showing both the alternative and the signature distributions obtained for a specific signature

The function generates a graph of all the enrichment results obtained during the permutation step for the selected signature and the specified samples. The graph also includes a compact display of the continuous distribution of the values for each sample.

Usage

plotPermutationSamplesDistribution(
  x,
  signature,
  samples,
  violinColor = "black",
  pointColor = "black",
  positionJitter = 0.2,
  alpha = 0.25,
  size = 0.95,
  seed = NA
)

Arguments

x

a list of class "SplitTypeRdata", the output object from runSubtypingfunction, to be graphed.

signature

a character string representing the signature that will be used to create the graph. The signature must be present in the object.

samples

a list of character string representing the names of the samples that will be used to create the graph. The samples must be present in the object.

violinColor

a character string representing the color of the line for the kernel density distributions in the graph. Default: "black".

pointColor

a character string representing the color of the dots representing the enrichment scores for the selected samples in the graph. Default: "black".

positionJitter

a numeric representing the amount of vertical and horizontal jitter added to the position of the points on the graph. Default: 0.20.

alpha

a numeric representing the amount of the opacity of the points on the graph. If NA, the color is completely transparent. Default: 0.25.

size

a numeric that represents the size of the points on the graph. Default: 0.95.

seed

a integer that will be used as seed to make the jitter reproducible. If NA, the seed is initialized with a random value. Default: NA.

Value

a ggplot object that contains all the enrichment scores obtained through the permutation step with the kernel desntiy distributions for the selected samples and the selected signature.

Author

Astrid Deschênes

Examples

## Loading signatures
data("signaturesDemo")

## Load demo normalized expected counts for 30 patients
data("expNormalCountsDemo")

## Fix seed for reproducibility
set.seed(1221)

## Run classification on the 30 patients using 20 permutations on 75% of 
## the dataset, and 10 points per patient for the up-scaling step
results <- runSubtypingBimodal(geneLists=signaturesDemo, 
    expectedCountsMatrix=expNormalCountsDemo, 
    permRatio=0.75, permNbr=30, upscaleNbr=5)
    
## Graph the enrichment results from the permutation for 3 samples
plotPermutationSamplesDistribution(x=results, 
    signature="Basal-like",
    samples=c("Patient_9", "Patient_25", "Patient_29"), 
    violinColor="darkred", pointColor="darkviolet", positionJitter=0.20, 
    size=1.2, seed=121)