Description Usage Arguments Author(s) References Examples
compute AUC values for each test.
1 2 3 | 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.
1 2 3 | data <- generateData(EntityCount=200)
test.obj <- testDATs(data,DE.methods="DESeq",nor.methods="default")
auc.obj <- computeAUC(test.obj)
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