multiplePRCPlot: Generate a plot with multiple PRC curves

In MetaIntegrator: Meta-Analysis of Gene Expression Data

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

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).

Value

Returns a ggplot PRC plot for all datasets

Author(s)

Aditya M. Rao, Andrew B. Liu

See Also

prcPlot, multipleROCPlot

Examples

multiplePRCPlot(tinyMetaObject, filterObject = 
   tinyMetaObject$filterResults$pValueFDR0.05_es0_nStudies1_looaTRUE_hetero0)

MetaIntegrator documentation built on March 26, 2020, 6:29 p.m.