computeAUC {EDDA} | R Documentation |
compute AUC values for each test.
computeAUC(obj,cutoff=1,numCores=10, DE.methods=c("Cuffdiff","DESeq","baySeq","edgeR","MetaStats","NOISeq"), nor.methods=c("default","Mode","UQN","NDE"))
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). |
Li Juntao, and Luo Huaien
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.
data <- generateData(EntityCount=200) test.obj <- testDATs(data,DE.methods="DESeq",nor.methods="default") auc.obj <- computeAUC(test.obj)