computeAUC: compute AUC values.

In EDDA: Experimental Design in Differential Abundance analysis

Description

compute AUC values for each test.

Usage

computeAUC(obj,cutoff=1,numCores=10,
		DE.methods=c("Cuffdiff","DESeq","baySeq","edgeR","MetaStats","NOISeq"), 
		nor.methods=c("default","Mode","UQN","NDE")) 

Arguments

obj	Object from testDATs().
cutoff	cutoff for ROC curve. Default is 1.
numCores	Number of cores for parallelization. Default is 10.
DE.methods	Method list for differential abundance tests. Methods currently available include "Cuffdiff","DESeq", "baySeq","edgeR","MetaStats","NOISeq".
nor.methods	Normalization method list. Methods currently available include "default"(default normalization for each DE method), "Mode"(Mode normalization),"UQN"(Upper quartile normalization),"NDE"(non-differential expression normalization).

Author(s)

Li Juntao, and Luo Huaien

References

Luo Huaien, Li Juntao,Chia Kuan Hui Burton, Shyam Prabhakar, Paul Robson, Niranjan Nagarajan, The importance of study design for detecting differentially abundant features in high-throughput experiments, under review.

Examples

data <- generateData(EntityCount=200)
test.obj <- testDATs(data,DE.methods="DESeq",nor.methods="default")
auc.obj  <- computeAUC(test.obj)

EDDA documentation built on Nov. 8, 2020, 5:44 p.m.