rocPlot: Plot ROC Curve for a Dataset

In MetaIntegrator: Meta-Analysis of Gene Expression Data

Description

rocPlot will plot an ROC curve (and return the AUC) that describes how well a gene signature (as defined in a filterObject) classifies groups in a dataset (in the form of a datasetObject).

Usage

rocPlot(filterObject, datasetObject, title = datasetObject$formattedName)

Arguments

filterObject	a MetaFilter object containing the signature genes that will be used for calculation of the ROC plot.
datasetObject	a Dataset object for group comparison in the ROC plot. (At least, must have a $expr of probe-level data, $keys of probe:gene mappings, and $class of two-class labels.)
title	Title for the ROC plot.

Details

Evaluates the ability of a given gene set to separate two classes. The gene set is evaluated as a Z-score of the difference in means between the positive genes and the negative genes (see calculateScore). Returns a standard ROC plot, plus AUC with 95% CI (calculated according to Hanley method).

Value

Returns a ggplot2 plot object

Author(s)

Timothy E. Sweeney

See Also

calculateScore, calculateROC

Examples

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

Example output

Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)
Used  12 of  12  pos genes, and  12  of  12  neg genes 
For dataset Whole Blood Study 1, AUC = 0.856

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