EDDA (version 1.10.0)

computeAUC: compute AUC values.

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).

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)