computeAUC {EDDA} | R Documentation

## 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).

### 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)

[Package EDDA version 1.26.0 Index]