corMatToAUC: Evaluate meanAUC/meanAUPR/medianAUC/medianAUPR of Recovering...

Description Usage Arguments Value

View source: R/Network.R


Given the task of recovering known pathways/network, calculate the average AUC value and average AUPR value based on gene correlation matrix and the gold stanard, e.g., the known pathway matrix.


corMatToAUC(data, GS, objective = "mean.AUC")



Correlation matrix with the dimension gene-by-gene


GS stands for gold standard which is a binary matrix with the dimension gene-by-pathway. The number of rows should match that of data and the genes should be in the same order. This pathway matrix can be obtained from pathway databased, e.g., MSigDB

  • "mean.AUC" (default option): Output is c(meanAUPR, meanAUC) and the objective is meanAUC (average AUC across all pathways)

  • "mean.AUPR"" : Output is c(meanAUC, meanAUPR) and the objective is meanAUPR

  • "median.AUC" : Output is c(medianAUPR, medianAUC) and the objective is medianAUC (median AUC across all pathways)

  • "median.AUPR" : Output is c(medianAUC, medianAUPR) and the objective is medianAUPR


A vector c(meanAUPR, meanAUC). By default, the biological objective is the average AUC acorss all pathways. The output will change based on objective

wgmao/DataRemix documentation built on Aug. 6, 2020, 4:49 p.m.