multiplePRCPlot: Generate a plot with multiple PRC curves

Description

for each dataset in the metaObject, prcPlot will return a ggplot of a Precision-Recall curve (and return the AUPRC) that describes how well a gene signature (as defined in a filterObject) classifies groups in a dataset (in the form of a datasetObject).

Usage

multiplePRCPlot(metaObject, filterObject, title = NULL,
  legend.names = NULL, curveColors = NULL, size = 22)

Arguments

metaObject

a metaObject which must have metaObject$originalData populated with a list of datasetObjects that will be used for discovery

filterObject

a metaFilter object containing the signature genes that will be used for calculating the score

title

title of the plot

legend.names

the name listed for each dataset in the legend (default: the datasetObject$formattedName for each dataset)

curveColors

Graphical: vector of colors for the PRC curves

size

use this to easily increase or decrease the size of all the text in the plot

Value

Returns a ggplot PRC plot for all datasets

Details

Each PRC plot evaluates the ability of a given gene set to separate two classes. As opposed to ROC curves, PRC curves are more sensitive to class imbalances. The gene set is evaluated as a Z-score of the difference in means between the positive genes and the negative genes (see calculateScore).

Examples

Run this code

# NOT RUN {
multiplePRCPlot(tinyMetaObject, filterObject = 
   tinyMetaObject$filterResults$pValueFDR0.05_es0_nStudies1_looaTRUE_hetero0)
# }