prcPlot: Plot the PRC Curve for a Dataset

In MetaIntegrator: Meta-Analysis of Gene Expression Data

Description

prcPlot will plot 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

prcPlot(filterObject, datasetObject, title = datasetObject$formattedName,
  subtitle = NULL, textSize = NULL, rounding = 3,
  curveColors = "red", legend = TRUE, PRC.lty = 1, PRC.lwd = 1,
  backgroundColor = "gray93", grid.marks = 0.1, grid.color = "white",
  grid.lty = 1, grid.lwd = 0.9, legend.lty = 0, cex.main = 1,
  cex.subtitle = 0.9)

Arguments

filterObject	a metaFilter object containing the signature genes that will be used for calculating the score
datasetObject	a Dataset object for group comparison in the PRC plot. (At least, must have a $expr of probe-level data, $keys of probe:gene mappings, and $class of two-class labels.)
title	title of the plot (default: datasetObject$formattedName)
subtitle	subtitle of the figure
textSize	use this to easily increase or decrease the size of all the text in the plot
rounding	how many digits to round the AUPRC and CI to (default: 3)
curveColors	Graphical: the color for the PRC curves (default: "red")
legend	Graphical: if TRUE, a legend will be included
PRC.lty	Graphical: PRC curve line type
PRC.lwd	Graphical: PRC curve line width
backgroundColor	Graphical: background color of the plot
grid.marks	Graphical: increment between grid lines
grid.color	Graphical: grid line color
grid.lty	Graphical: grid line type
grid.lwd	Graphical: grid line width
legend.lty	Graphical: legend style (0 is no box, 1 is boxed legend)
cex.main	Graphical: title size
cex.subtitle	Graphical: subtitle size

Details

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 standard PRC plot, plus AUPRC with 95% CI (calculated with the trapezoid method).

Author(s)

Aditya M. Rao, Jiaying Toh

See Also

multiplePRCPlot, rocPlot

Examples

prcPlot(tinyMetaObject$filterResults[[1]], tinyMetaObject$originalData[[1]])

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